Solana gained considerable attention as one of the most popular blockchain platforms for deploying decentralized applications. Compared to Ethereum, however, we observe a lack of research on how Solana smart contract developers handle security, what challenges they encounter, and how this affects the overall security of the ecosystem.

To address this, we conducted the first comprehensive study on the Solana platform. Our study shows, quite alarmingly, that none of the participants could detect all important security vulnerabilities in a code review task and that 83% of the participants are likely to release vulnerable smart contracts. Our study also sheds light on the root causes of developers' challenges with Solana smart contract development, suggesting the need for better security guidance and resources. In spite of these challenges, our automated analysis on currently deployed Solana smart contracts surprisingly suggests that the prevalence of vulnerabilities - especially those pointed out as the most challenging in our developer study - is below 0.3%. We explore the causes of this counter-intuitive resilience and show that frameworks, such as Anchor, are positively aiding Solana developers - even those unmindful of security - in deploying secure contracts.

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process.

Trusted execution environments provide strong security guarantees, like isolation and confidentiality, but are not immune from memory-safety violations. Our investigation of public trusted execution environment code based on symbolic execution and fuzzing reveals subtle memory safety issues.

Emerging non-volatile memories (NVMs) are promising candidates to substitute conventional memories due to their low access latency, high integration density, and non-volatility. These superior properties stem from the memristor representing the centerpiece of each memory cell and is branded as the fourth fundamental circuit element. Memristors encode information in the form of its resistance by altering the physical characteristics of their filament. Hence, each memristor can store multiple bits increasing the memory density and positioning it as a potential candidate to replace DRAM and SRAM-based memories, such as caches. However, new security risks arise with the benefits of these emerging technologies, like the recent NeuroHammer attack, which allows adversaries to deliberately flip bits in ReRAMs. While NeuroHammer has been shown to flip single bits within memristive crossbar arrays, the system-level impact remains unclear. Considering the significance of the Rowhammer attack on conventional DRAMs, NeuroHammer can potentially cause crucial damage to applications taking advantage of emerging memory technologies. To answer this question, we introduce NVgem5, a versatile system-level simulator based on gem5. NVgem5 is capable of injecting bit-flips in eNVMs originating from NeuroHammer. Our experiments evaluate the impact of the NeuroHammer attack on main and cache memories. In particular, we demonstrate a single-bit fault attack on cache memories leaking the secret key used during the computation of RSA signatures. Our findings highlight the need for improved hardware security measures to mitigate the risk of hardware-level attacks in computing systems based on eNVMs.

Solana has quickly emerged as a popular platform for building decentralized applications (DApps), such as marketplaces for non- fungible tokens (NFTs). A key reason for its success are Solana’s low transaction fees and high performance, which is achieved in part due to its stateless programming model. Although the litera- ture features extensive tooling support for smart contract security, current solutions are largely tailored for the Ethereum Virtual Ma- chine. Unfortunately, the very stateless nature of Solana’s execution environment introduces novel attack patterns specific to Solana requiring a rethinking for building vulnerability analysis methods. In this paper, we address this gap and propose FuzzDelSol, the first binary-only coverage-guided fuzzing architecture for Solana smart contracts. FuzzDelSol faithfully models runtime specifics such as smart contract interactions. Moreover, since source code is not available for the large majority of Solana contracts, FuzzDelSol operates on the contract’s binary code. Hence, due to the lack of semantic information, we carefully extracted low-level program and state information to develop a diverse set of bug oracles covering all major bug classes in Solana. Our extensive evaluation on 6049 smart contracts shows that FuzzDelSol’s bug oracles finds impactful vulnerabilities with a high precision and recall. To the best of our knowledge, this is the largest evaluation of the security landscape on the Solana mainnet.

Remote attestation allows validating the trustworthiness of a remote device. Existing attestation schemes either require hardware changes, trusted computing components, or rely on strict timing constraints. In this paper, we present a novel remote attestation approach, called DMA’n’Play, that tackles these practical limitations by leveraging DMA (direct memory access). Since DMA does not require CPU time, DMA’n’Play even allows attestation of devices with real-time constraints. To prevent the exploitation of side-channels which potentially could determine if the attestation is running, we developed DMA’n’Play To-Go, a small, mobile attestation device that can be plugged into the attested device. We evaluated DMA’n’Play on two real-world devices, namely a syringe pump and a drone. Our evaluation shows that DMA’n’Play adds negligible performance overhead and prevents dataonly attacks, by validating critical data in memory.

From the perspective of end-users, IoT devices behave like a black box: As long as they work as intended, the user will not detect any compromise. The user has minimal control over the software. Hence, it is very likely that the user misses that illegal recordings and transmissions occur if a security camera or a smart speaker is hacked. In this paper, we present SCAtt-man, the first remote attestation scheme that is specifically designed with the user in mind. SCAtt-man deploys software-based attestation to check the integrity of remote devices, allowing users to verify the integrity of IoT devices with their smartphone. The key novelty of SCAtt-man resides in the utilization of user-observable side-channels such as light or sound in the attestation protocol.
Our proof-of-concept implementation targets a smart speaker and an attestation protocol that is based on a data-over-sound protocol. Our evaluation demonstrates the effectiveness of SCAtt-man against a variety of attacks and its usability based on a comprehensive user study with 20 participants.

Smart contracts are programs which encode business logic and execute on the blockchain. While Ethereum is the most popular blockchain platform for smart contracts, an increasing number of new blockchain platforms are also able to support smart contract execution (e.g., Solana or Cardano). Security vulnerabilities in Ethereum smart contracts have demonstrated that writing secure smart contracts is highly challenging. This is exacerbated by the fact that the exploitation of buggy smart contracts seems disproportionately easier compared to exploiting classic PC software.

In this tutorial, we overview a number of smart contract vulnerabilities focusing on the Ethereum ecosystem. We also provide an introduction to the de-facto smart contract programming language Solidity and provide a comprehensive hands-on lab tutorial that involves analyzing vulnerable smart contracts, developing proof-of-concept exploits as well as introducing security analysis tools for testing smart contracts.

Intel SGX is a security feature of processors that allows running software in enclaves, isolated from the operating system. Even an attacker with full control of the computer system cannot inspect these enclaves. This makes SGX enclaves an
adequate solution to store and process highly sensitive data like encryption keys. However, these enclaves are still vulnerable to standard software attacks. While SGX allows static attestation, i.e., validating the integrity of the program code and data in the enclave, static attestation cannot detect run-time attacks.
We present SALSA , the first solution to allow run-time attestation of SGX enclaves. To show its applicability, we use SALSA to implement a video streaming service that uses an SGX enclave to decode the video stream. When a compromise of the SGX enclave is detected, the streaming of the video instantaneously stops. This shows a practical use-case for runtime attestation of SGX enclaves. In the evaluation, we show that the performance of this setup is sufficient to attest a live video streaming service.

Memory safety in complex applications implemented in unsafe programming languages such as C/C++ is still an unresolved problem in practice. Such applications were often developed in an ad-hoc, security-ignorant fashion, and thus they contain many types of security issues. Many different types of defenses have been proposed in the past to mitigate these problems, some of which are even widely used in practice. However, advanced attacks are still able to circumvent these defenses, and the arms race is not (yet) over. On the defensive side, the most promising next step is a tighter integration of the hardware and software level: modern mitigation techniques are either accelerated using hardware extensions or implemented in the hardware by extensions of the instruction set architecture (ISA). In particular, memory tagging, as proposed by ARM or SPARC, promises to solve many issues for practical memory safety. Unfortunately, Intel x86-64, which represents the most important ISA for both the desktop and server domain, lacks support for hardware-accelerated memory tagging, so memory tagging is not considered practical for this platform. In this paper, we present the design and implementation of an efficient, software-only pointer tagging scheme for Intel x86-64 based on a novel metadata embedding scheme. The basic idea is to alias multiple virtual pages to one physical page so that we can efficiently embed tag bits into a pointer. Furthermore, we introduce several optimizations that significantly reduce the performance impact of this approach to memory tagging. Based on this scheme, we propose a novel use-after-free mitigation scheme, called xTag, that offers better performance and strong security properties compared to state-of-the-art methods. We also show how double-free vulnerabilities can be mitigated. Our approach is highly compatible, allowing pointers to be passed back and forth between instrumented and non-instrumented code without losing metadata, and it is even compatible with inline assembly. We conclude that building exploit mitigation mechanisms on top of our memory tagging scheme is feasible on Intel x86-64, as demonstrated by the effective prevention of use-after-free bugs in the Firefox web browser.

Recent attacks exploiting errors in smart contract code had devastating consequences thereby questioning the benefits of this technology. It is currently highly challenging to fix errors and deploy a patched contract in time. Instant patching is especially important since smart contracts are always online due to the distributed nature of blockchain systems. They also manage considerable amounts of assets, which are at risk and often beyond recovery after an attack. Existing solutions to upgrade smart contracts depend on manual and error-prone processes. This paper presents a framework, called EVMPatch, to instantly and automatically patch faulty smart contracts. EVMPatch features a bytecode rewriting engine for the popular Ethereum blockchain, and transparently/automatically rewrites common off-the-shelf contracts to upgradable contracts. The proof-of-concept implementation of EVMPatch automatically hardens smart contracts that are vulnerable to integer over/underflows and access control errors, but can be easily extended to cover more bug classes. Our evaluation on 14,000 real-world contracts demonstrates that our approach successfully blocks attack transactions launched on contracts, while keeping the intended functionality of the contract intact. We perform a study with experienced software developers, showing that EVMPatch is practical, and reduces the time for converting a given Solidity smart contract to an upgradable contract by 97.6 %, while ensuring functional equivalence to the original contract.

Memory corruption attacks are a pre-dominant attack vector against IoT devices. Simply updating vulnerable IoT software is not always possible due to unacceptable downtime and a required reboot. These side-effects must be avoided for highly-available embedded systems such as medical devices and, generally speaking, for any embedded system with real-time constraints.
To avoid downtime and reboot of a system, previous research has introduced the concept of hotpatching. However, the existing approaches cannot be applied to resource-constrained IoT devices. Furthermore, possible hardware-related issues have not been addressed, i.e., the inability to directly modify the firmware image due to read-only memory.

In this paper, we present the design and implementation of HERA (Hotpatching of Embedded Real-time Applications) which utilizes hardware-based built-in features of commodity Cortex-M microcontrollers to perform hotpatching of embedded systems. HERA preserves hard real-time constraints while keeping the additional resource usage to a minimum. In a case study, we apply HERA to two vulnerable medical devices. Furthermore, we leverage HERA to patch an existing vulnerability in the FreeRTOS operating system. These applications demonstrate the high practicality and efficiency of our approach.

The field of computer hardware stands at the verge of a revolution driven by recent breakthroughs in emerging nanodevices. “Nano Security” is a new Priority Program recently approved by DFG, the German Research Council. This initial-stage project initiative at the crossroads of nano-electronics and hardware-oriented security includes 11 projects with a total of 23 Principal Investigators from 18 German institutions. It considers the interplay between security and nano-electronics, focusing on a dichotomy which emerging nano-devices (and their architectural implications) have on system security. The projects within the Priority Program consider both: potential security threats and vulnerabilities stemming from novel nano-electronics, and innovative approaches to establishing and improving system security based on nano-electronics. This paper provides an overview of the Priority Program's overall philosophy and discusses the scientific objectives of its individual projects.

Intel's Software Guard Extensions (SGX) introduced new instructions to switch the processor to enclave mode which protects it from introspection. While the enclave mode strongly protects the memory and the state of the processor, it cannot withstand memory corruption errors inside the enclave code. In this paper, we show that the attack surface of SGX enclaves provides new challenges for enclave developers as exploitable memory corruption vulnerabilities are easily introduced into enclave code. We develop TeeRex to automatically analyze enclave binary code for vulnerabilities introduced at the host-to-enclave boundary by means of symbolic execution. Our evaluation on public enclave binaries reveal that many of them suffer from memory corruption errors allowing an attacker to corrupt function pointers or perform arbitrary memory writes. As we will show, TeeRex features a specifically tailored framework for SGX enclaves that allows simple proof-of-concept exploit construction to assess the discovered vulnerabilities. Our findings reveal vulnerabilities in multiple enclaves, including enclaves developed by Intel, Baidu, and WolfSSL, as well as biometric fingerprint software deployed on popular laptop brands.

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Modern cars increasingly deploy complex software systems consisting of millions of lines of code that may be subject to cyber attacks. An infamous example is the Jeep hack which allowed an attacker to remotely control the car engine by just exploiting a software bug in the infotainment system. The digitalization and connectivity of modern cars demands a rethinking of car safety as security breaches now affect the driver's safety. To address the new threat landscape, we develop a novel concept that simultaneously addresses both car safety and security based on the arising blockchain technology, which we mainly exploit to ensure integrity. Previous related work exploited the blockchain for the purpose of forensics, where vehicle data is stored on an externally shared ledger that is accessible by authorized third parties. However, those approaches cannot ensure integrity of information used by the vehicle's components. In contrast, we propose a blockchain-based architecture based on a shared ledger inside the car, where each ECU can act as a miner and shares its information with other ECUs. The architecture does not only improve the integrity of information for forensics. Some algorithms, e.g. the recognition of dangerous situations, are adaptive and can be improved using for example sensor data. Using our architecture, we ensure that those algorithms only take verified and correct information as input.

Recently, a number of existing blockchain systems have witnessed major bugs and vulnerabilities within smart contracts. Although the literature features a number of proposals for securing smart contracts, these proposals mostly focus on proving the correctness or absence of a certain type of vulnerability within a contract, but cannot protect deployed (legacy) contracts from being exploited. In this paper, we address this problem in the context of re-entrancy exploits and propose a novel smart contract security technology, dubbed Sereum (Secure Ethereum), which protects existing, deployed contracts against re-entrancy attacks in a backwards compatible way based on run-time monitoring and validation. Sereum does neither require any modification nor any semantic knowledge of existing contracts. By means of implementation and evaluation using the Ethereum blockchain, we show that Sereum covers the actual execution flow of a smart contract to accurately detect and prevent attacks with a false positive rate as small as 0.06% and with negligible run-time overhead. As a by-product, we develop three advanced re-entrancy attacks to demonstrate the limitations of existing offline vulnerability analysis tools.

Fuzzing is a well-known technique for automatically testing the robustness of software and its susceptibility to security-critical errors. Recently, many new and improved fuzzers have been presented. One critical aspect of any new fuzzer is its overall performance. However, given that there exist no standardized fuzzing evaluation methodology, we observe significant discrepancy in evaluation results making it highly challenging to  compare fuzzing techniques.

To tackle this deficiency, we developed a new framework, called FETA, which automatically evaluates fuzzers based on a fixed and comprehensive test set enabling objective and general comparison of performance results. We apply FETA to various recently released academic and non-academic fuzzers, eventually resulting in a large scale evaluation of the current state-of-the-art fuzzing approaches.

Hardware security architectures and primitives are becoming increasingly important in practice providing trust anchors and trusted execution environment to protect modern software systems. Over the past two decades we have witnessed various hardware security solutions and trends from Trusted Platform Modules (TPM), performance counters for security, ARM's TrustZone, and Physically Unclonable Functions (PUFs), to very recent advances such as Intel's Software Guard Extension (SGX). Unfortunately, these solutions are rarely used by third party developers, make strong trust assumptions (including in manufacturers), are too expensive for small constrained devices, do not easily scale, or suffer from information leakage. Academic research has proposed a variety of solutions, in hardware security architectures, these advancements are rarely deployed in practice.

Intel Software Guard Extensions (SGX) isolate security-critical code inside a protected memory area called enclave. Previous research on SGX has demonstrated that memory corruption vulnerabilities within enclave code can be exploited to extract secret keys and bypass remote attestation. However, these attacks require kernel privileges, and rely on frequently probing enclave code which results in many enclave crashes. Further, they assume a constant, not randomized memory layout. In this paper, we present novel exploitation techniques against SGX that do not require any enclave crashes and work in the presence of existing SGX randomization approaches such as SGX-Shield. A key contribution of our attacks is that they work under weak adversarial assumptions, e.g., not requiring kernel privileges. In fact, they can be applied to any enclave that is developed with the standard Intel SGX SDK on either Linux or Windows.

Control-Flow Integrity (CFI) is a promising and general defense against control-flow hijacking with formal underpinnings. A key insight from the extensive research on CFI is that its effectiveness depends on the precision and coverage of a program's Control-Flow Graph (CFG). Since precise CFG generation is highly challenging and often difficult, many CFI schemes rely on brittle heuristics and imprecise, coarse-grained CFGs. Furthermore, comprehensive, fine-grained CFI defenses implemented purely in software incur overheads that are unacceptably high. In this chapter, we first specify a CFI model that captures many known CFI techniques, including stateless and stateful approaches as well as fine-grained and coarse-grained CFI policies.We then design and implement a novel hardwareenhanced CFI. Key to this approach is a set of dedicated CFI instructions that can losslessly enforce any CFG and diverse CFI policies within our model. Moreover, we fully support multi-tasking and shared libraries, prevent various forms of codereuse attacks, and allow code protected with CFI to interoperate with unprotected legacy code. Our prototype implementation on the SPARC LEON3 is highly efficient with a performance overhead of 1.75% on average when applied to several SPECInt2006 benchmarks and 0.5% when applied to EEMBC's CoreMark benchmark.

Operating system kernels are appealing attack targets: compromising the kernel usually allows attackers to bypass all deployed security mechanisms and take control over the entire system. Commodity kernels, like Linux, are written in low-level programming languages that offer only limited type and memory-safety guarantees, enabling adversaries to launch sophisticated run-time attacks against the kernel by exploiting memory-corruption vulnerabilities. Many defenses have been proposed to protect operating systems at run time, such as control-flow integrity (CFI). However, the goal of these run-time monitors is to prevent exploitation as a symptom of memory corruption, rather than eliminating the underlying root cause, i.e., bugs in the kernel code. While finding bugs can be automated, e.g., using static analysis, all existing approaches are limited to local, intra-procedural checks, and face severe scalability challenges due to the large kernel code base. Consequently, there currently exist no tools for conducting global static analysis of operating system kernels. In this paper, we present K-Miner, a new framework to efficiently analyze large, commodity operating system kernels like Linux. Our novel approach exploits the highly standardized interface structure of the kernel code to enable scalable pointer analysis and conduct global, context-sensitive analysis. Through our inter-procedural analysis we show that K-Miner systematically and reliably uncovers several different classes of memory-corruption vulnerabilities, such as dangling pointers, user-after-free, double-free, and double-lock vulnerabilities. We thoroughly evaluate our extensible analysis framework, which leverages the popular and widely used LLVM compiler suite, for the current Linux kernel and demonstrate its effectiveness by reporting several memory-corruption vulnerabilities.

Just-in-time return-oriented programming (JIT-ROP) is a powerful memory corruption attack that bypasses various forms of code randomization. Execute-only memory (XOM) can potentially prevent these attacks, but requires source code. In contrast, destructive code reads (DCR) provide a trade-off between security and legacy compatibility. The common belief is that DCR provides strong protection if combined with a high-entropy code randomization. The contribution of this paper is twofold: first, we demonstrate that DCR can be bypassed regardless of the underlying code randomization scheme. To this end, we show novel, generic attacks that infer the code layout for highly randomized program code. Second, we present the design and implementation of BGDX (Byte-Granular DCR and XOM), a novel mitigation technique that protects legacy binaries against code-inference attacks. BGDX enforces memory permissions on a byte-granular level allowing us to combine DCR and XOM for legacy, off-the-shelf binaries. Our evaluation shows that BGDX is not only effective, but highly efficient, imposing only a geometric mean performance overhead of 3.95% on SPEC.

With the increasing scale of deployment of Internet of Things (IoT), concerns about IoT security have become more urgent. In particular, memory corruption attacks play a predominant role as they allow remote compromise of IoT devices. Control-flow integrity (CFI) is a promising and generic defense technique against these attacks. However, given the nature of IoT deployments, existing protection mechanisms for traditional computing environments (including CFI) need to be adapted to the IoT setting. In this paper, we describe the challenges of enabling CFI on microcontroller (MCU) based IoT devices. We then present CaRE, the first interrupt-aware CFI scheme for low-end MCUs. CaRE uses a novel way of protecting the CFI metadata by leveraging TrustZone-M security extensions introduced in the ARMv8-M architecture. Its binary instrumentation approach preserves the memory layout of the target MCU software, allowing pre-built bare-metal binary code to be protected by CaRE. We describe our implementation on a Cortex-M Prototyping System and demonstrate that CaRE is secure while imposing acceptable performance and memory impact.

Rowhammer is a hardware bug that can be exploited to implement privilege escalation and remote code execution attacks. Previous proposals on rowhammer mitigations either require hardware changes or follow heuristic-based approaches (based on CPU performance counters). To date, there exists no instant protection against rowhammer attacks on legacy systems.

In this paper, we present the design and implementation of a practical and efficient software-only defense against rowhammer attacks. Our defense, called CATT, prevents the attacker from leveraging rowhammer to corrupt kernel memory from user mode. To do so, we extend the physical memory allocator of the OS to physically isolate the memory of the kernel and user space. We implemented CATT on x86 and ARM to mitigate rowhammer-based kernel exploits. Our extensive evaluation shows that our mitigation (i) can stop available real-world rowhammer attacks, (ii) imposes virtually no runtime overhead for common user and kernel benchmarks as well as commonly used applications, and (iii) does not affect the stability of the overall system.

Attacks targeting software on embedded systems are becoming increasingly prevalent. Remote attestation is a mechanism that allows establishing trust in embedded devices. However, existing attestation schemes are either static and cannot detect control-flow attacks, or require instrumentation of software incurring high performance overheads. To overcome these limitations, we present LO-FAT, the first practical hardware-based approach to control-flow attestation. By leveraging existing processor hardware features and commonly-used IP blocks, our approach enables efficient control-flow attestation without requiring software instrumentation. We show that our proof-of-concept implementation based on a RISC-V SoC incurs no processor stalls and requires reasonable area overhead.

Kernel exploits constitute a powerful attack class allowing attackers to gain full control over a system. Various kernel hardening solutions have been proposed or deployed in practice to protect the kernel against code injection (e.g., DEP) or code-reuse exploits (e.g., CFI). However, the security of all these hardening techniques relies heavily on the assumption that kernel page tables cannot be manipulated, e.g., by means of dataonly attacks. Ensuring kernel page tables integrity is not only essential for kernel security but also a challenging task in practice since existing solutions require hardware trust anchors, costly hypervisors, or inefficient integrity checks. 

In this paper, we first motivate the importance of protecting kernel page tables by presenting a data-only attack against page tables to bypass the recently released CFI-based (Linux) kernel hardening technique RAP. Thereafter, we present the design and implementation of PT-Rand, the first practical solution to protect kernel page tables that does not suffer from the mentioned deficiencies of previous proposals. PT-Rand randomizes the location of page tables and tackles a number of challenges to ensure that the location of page tables is not leaked. This effectively prevents the attacker from manipulating access permissions of code pages, thereby enabling secure enforcement of kernel exploit mitigation technologies such as CFI. We extensively evaluate our prototype implementation of PT-Rand for the current Linux kernel on the popular Linux distribution Debian and report a low overhead of 0.22% for common benchmarks. Moreover, we combine RAP with PT-Rand to protect RAP against data-only attacks on kernel page tables.

Memory corruption vulnerabilities not only allow modification of control data and injection of malicious payloads; they also allow adversaries to reconnoiter a diversified program, customize a payload, and ultimately bypass code randomization defenses. In response, researchers have proposed and built various leakage-resilient defenses against code reuse. Leakage-resilient defenses use memory protection techniques to prevent adversaries from directly reading code as well as pointer indirection or encryption techniques to decouple code pointers from the randomized code layout, avoiding indirect leakage. In this paper, we show that although current code pointer protections do prevent leakage per se, they are fundamentally unable to stop code reuse. Specifically, we demonstrate a new class of attacks we call address-oblivious code reuse that bypasses state-of-the-art leakage-resilience techniques by profiling and reusing protected code pointers, without leaking the code layout. We show that an attacker can accurately identify protected code pointers of interest and mount code-reuse attacks at the abstraction level of pointers without requiring any knowledge of code addresses. We analyze the prevalence of opportunities for such attacks in popular code bases and build three real-world exploits against Nginx and Apache to demonstrate their practicality. We analyze recently proposed leakage resilient defenses and show that they are vulnerable to address oblivious code reuse. Our findings indicate that because of the prevalence of code pointers in realistic programs and the fundamental need to expose them to    read    operations (even indirectly), diversity defenses face a fundamental design challenge in mitigating such attacks.

Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices. It allows a trusted party (verifier) to learn the state of a remote, and potentially malware-infected, device (prover). Most existing approaches are static in nature and only check whether benign software is initially loaded on the prover. However, they are vulnerable to runtime attacks that hijack the application's control or data flow, e.g., via return-oriented programming or data-oriented exploits. As a concrete step towards more comprehensive runtime remote attestation, we present the design and implementation of Control-FLow ATtestation (C-FLAT) that enables remote attestation of an application's control-flow path, without requiring the source code. We describe a full prototype implementation of C-FLAT on Raspberry Pi using its ARM TrustZone hardware security extensions. We evaluate C-FLAT's performance using a real-world embedded (cyber-physical) application, and demonstrate its efficacy against control-flow hijacking attacks.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

The emerging and much-touted Internet of Things (IoT) presents a variety of security and privacy challenges. Prominent among them is the establishment of trust in remote IoT devices, which is typically attained via remote attestation, a distinct security service that aims to ascertain the current state of a potentially compromised remote device. Remote attestation ranges from relatively heavy-weight secure hardware-based techniques, to light-weight software-based ones, and also includes approaches that blend software (e.g., control-flow integrity) and hardware features (e.g., PUFs). In this paper, we survey the landscape of state-of-the-art attestation techniques from the IoT device perspective and argue that most of them have a role to play in IoT trust establishment.

Attack techniques based on code reuse continue to enable real-world exploits bypassing all current mitigations. Code randomization defenses greatly improve resilience against code reuse. Unfortunately, sophisticated modern attacks such as JIT-ROP can circumvent randomization by discovering the actual code layout on the target and relocating the attack payload on the fly. Hence, effective code randomization additionally requires that the code layout cannot be leaked to adversaries.

Previous approaches to leakage-resilient diversity have either relied on hardware features that are not available in all processors, particularly resource-limited processors commonly found in mobile devices, or they have had high memory overheads. We introduce a code randomization technique that avoids these limitations and scales down to mobile and embedded devices: Leakage-Resilient Layout Randomization (LR^2).

Whereas previous solutions have relied on virtualization, x86 segmentation, or virtual memory support, LR^2 merely requires the underlying processor to enforce a W XOR X policy---a feature that is virtually ubiquitous in modern processors, including mobile and embedded variants. Our evaluation shows that LR^2 provides the same security as existing virtualization-based solutions while avoiding design decisions that would prevent deployment on less capable yet equally vulnerable systems. Although we enforce execute-only permissions in software, LR^2 is as efficient as the best-in-class virtualization-based solution.

This book provides an in-depth look at return-oriented programming attacks. It explores several conventional return-oriented programming attacks and analyzes the effectiveness of defense techniques including address space layout randomization (ASLR) and the control-flow restrictions implemented in security watchdogs such as Microsoft EMET.

Chapters also explain the principle of control-flow integrity (CFI), highlight the benefits of CFI and discuss its current weaknesses. Several improved and sophisticated return-oriented programming attack techniques such as just-in-time return-oriented programming are presented.

Building Secure Defenses against Code-Reuse Attacks is an excellent reference tool for researchers, programmers and professionals working in the security field. It provides advanced-level students studying computer science with a comprehensive overview and clear understanding of important runtime attacks.

Whereas user-facing applications are often written in modern languages, the firmware, operating system, support libraries, and virtual machines that underpin just about any modern computer system are still written in low-level languages that value flexibility and performance over convenience and safety. Programming errors in low-level code are often exploitable and can, in the worst case, give adversaries unfettered access to the compromised host system.

This book provides an introduction to and overview of automatic software diversity techniques that, in one way or another, use randomization to greatly increase the difficulty of exploiting the vast amounts of low-level code in existence. Diversity-based defenses are motivated by the observation that a single attack will fail against multiple targets with unique attack surfaces. We introduce the many, often complementary, ways that one can diversify attack surfaces and provide an accessible guide to more than two decades worth of research on the topic. We also discuss techniques used in conjunction with diversity to prevent accidental disclosure of randomized program aspects and present an in-depth case study of one of our own diversification solutions.

Adversaries exploit memory corruption vulnerabilities to hijack a program's control flow and gain arbitrary code execution. One promising mitigation, control-flow integrity (CFI), has been the subject of extensive research in the past decade. One of the core findings is that adversaries can construct Turing-complete code-reuse attacks against coarse-grained CFI policies because they admit control flows that are not part of the original program. This insight led the research community to focus on fine-grained CFI implementations. In this paper we show how to exploit heap-based vulnerabilities to control the stack contents including security-critical values used to validate control-flow transfers. Our investigation shows that although program analysis and compiler-based mitigations reduce stack-based vulnerabilities, stack-based memory corruption remains an open problem. Using the Chromium web browser we demonstrate real-world attacks against various CFI implementations: 1) against CFI implementations under Windows 32-bit by exploiting unprotected context switches, and 2) against state-of-the-art fine-grained CFI implementations (IFCC and VTV) in the two premier open-source compilers under Unix-like operating systems. Both 32 and 64-bit x86 CFI checks are vulnerable to stack manipulation. Finally, we provide an exploit technique against the latest shadow stack implementation.

Code-reuse attacks continue to evolve and remain a severe threat to modern software. Recent research has proposed a variety of defenses with differing security, efficiency, and practicality characteristics. Whereas the majority of these solutions focus on specific code-reuse attack variants such as return-oriented programming (ROP), other attack variants that reuse whole functions, such as the classic return-into-libc, have received much less attention. Mitigating function-level code reuse is highly challenging because one needs to distinguish a legitimate call to a function from an illegitimate one. In fact, the recent counterfeit object-oriented programming (COOP) attack demonstrated that the majority of code-reuse defenses can be bypassed by reusing dynamically bound functions, i.e., functions that are accessed through global offset tables and virtual function tables, respectively.

In this paper, we first significantly improve and simplify the COOP attack. Based on a strong adversarial model, we then present the design and implementation of a comprehensive code-reuse defense which is resilient against reuse of dynamically-bound functions. In particular, we introduce two novel defense techniques: (i) a practical technique to randomize the layout of tables containing code pointers resilient to memory disclosure and (ii) booby trap insertion to mitigate the threat of brute-force attacks iterating over the randomized tables. Booby traps serve the dual purpose of preventing fault-analysis side channels and ensuring that each table has sufficiently many possible permutations. Our detailed evaluation demonstrates that our approach is secure, effective, and practical. We prevent realistic, COOP-style attacks against the Chromium web browser and report an average overhead of 1,1% on the SPEC CPU2006 benchmarks.

Detecting and preventing exploitation of memory corruption vulnerabilities is highly challenging. Until now, no countermeasure has been able to fully prevent sophisticated exploitation techniques, such as return-oriented programming (ROP). Recent control-flow integrity (CFI) defenses from Google and Microsoft can be bypassed by constructing a ROP payload that adheres to the control-flow constraints or by exploiting implementation flaws. Microsoft’s EMET has less overhead than full CFI, but offers less protection in return, and can be bypassed. Probabilistic countermeasures based on memory layout randomization (such as ASLR) are already in widespread use. However, the Pwn2own competitions have repeatedly demonstrated that attackers can bypass code randomization using memory leaks in browsers.

To reduce the impact of memory disclosure, recent defenses utilize execute-only memory. In this work we show that execute-only memory is insufficient and that these solutions can still be bypassed. In particular, we show how to determine the code layout by analyzing pointers in the heap and on the stack without ever reading the code.

On the defensive side, we build a comprehensive yet practical defense called Readactor that counters both direct reading of code and indirect layout disclosure through analysis of code pointers. We employ a thin hypervisor and a kernel patch to utilize true hardware execute-only memory, and prevent direct reading of code in Linux processes. We hide all code pointers in readable memory using a patched version of the LLVM compiler. We deploy a number of techniques to break ROP gadget chains and disorient the adversary. Specifically, we hide code pointers by converting them into direct jumps stored in execute-only memory to prevent indirect layout disclosure.

Our solution is efficient, because it activates previously unused hardware capabilities in modern x86 processors and is often faster than industry CFI implementations. Our solution is also highly practical; we were able to automatically apply our defense to the Chromium web browser. Finally, our solution is comprehensive; we also protect the dynamically generated code emitted by the V8 JavaScript JIT compiler.

Code-reuse attacks like return-oriented programming (ROP) pose a severe threat to modern software on diverse processor architectures. Designing practical and secure defenses against code-reuse attacks is highly challenging and currently subject to intense research. However, no secure and practical system-level solutions exist so far, since a large number of proposed defenses have been successfully bypassed. To tackle this attack, we present HAFIX (Hardware-Assisted Flow Integrity eXtension), a defense against code-reuse attacks exploiting backward edges (returns). HAFIX provides fine-grained and practical protection, and serves as an enabling technology for future control-flow integrity instantiations. This paper presents the implementation and evaluation of HAFIX for the Intel Siskiyou Peak and SPARC embedded system architectures, and demonstrates its security and efficiency in code-reuse protection while incurring only 2% performance overhead.

Code-reuse attacks such as return-oriented programming (ROP) pose a severe threat to modern software. Designing practical and effective defenses against code-reuse attacks is highly challenging. One line of defense builds upon fine-grained code diversification to prevent the adversary from constructing a reliable code-reuse attack. However, all solutions proposed so far are either vulnerable to memory disclosure or are impractical for deployment on commodity systems. In this paper, we address the deficiencies of existing solutions and present the first practical, fine-grained code randomization defense, called Readactor, resilient to both static and dynamic ROP attacks. We distinguish between direct memory disclosure, where the attacker reads code pages, and indirect memory disclosure, where attackers use code pointers on data pages to infer the code layout without reading code pages. Unlike previous work, Readactor resists both types of memory disclosure. Moreover, our technique protects both statically and dynamically generated code. We use a new compiler-based code generation paradigm that uses hardware features provided by modern CPUs to enable execute-only memory and hide code pointers from leakage to the adversary. Finally, our extensive evaluation shows that our approach is practical‐-we protect the entire Google Chromium browser and its V8 JIT compiler‐-and efficient with an average SPEC CPU2006 performance overhead of only 6.4%.

Code reuse attacks such as return-oriented programming (ROP) have become prevalent techniques to exploit memory corruption vulnerabilities in software programs. A variety of corresponding defenses has been proposed, of which some have already been successfully bypassed - and the arms race continues. In this paper, we perform a systematic assessment of recently proposed CFI solutions and other defenses against code reuse attacks in the context of C++. We demonstrate that many of these defenses that do not consider object-oriented C++ semantics precisely can be generically bypassed in practice. Our novel attack technique, denoted as counterfeit object-oriented programming (COOP), induces malicious program behavior by only invoking chains of existing C++ virtual functions in a program through corresponding existing call sites. COOP is Turing complete in realistic attack scenarios and we show its viability by developing sophisticated, real-world exploits for Internet Explorer 10 on Windows and Firefox 36 on Linux. Moreover, we show that even recently proposed defenses (CPS, T-VIP, vfGuard, and VTint) that specifically target C++ are vulnerable to COOP. We observe that constructing defenses resilient to COOP that do not require access to source code seems to be challenging. We believe that our investigation and results are helpful contributions to the design and implementation of future defenses against control-flow hijacking attacks.

Until very recently it was widely believed that iOS malware is effectively blocked by Apple’s vetting process and application sandboxing. However, the newly presented severe malicious app attacks (e.g., Jekyll) succeeded to undermine these protection measures and steal private data, post Twitter messages, send SMS, and make phone calls. Currently, no effective defenses against these attacks are known for iOS. The main goal of this paper is to systematically analyze the recent attacks against iOS sandboxing and provide a practical security framework for iOS app hardening which is fully independent of the Apple’s vetting process and particularly benefits enterprises to protect employees’ iOS devices. The contribution of this paper is twofold: First, we show a new and generalized attack that significantly reduces the complexity of the recent attacks against iOS sandboxing. Second, we present the design and implementation of a novel and efficient iOS app hardening service, XiOS, that enables fine-grained application sandboxing, and mitigates the existing as well as our new attacks. In contrast to previous work in this domain (on iOS security), our approach does not require to jailbreak the device. We demonstrate the efficiency and effectiveness of XiOS by conducting several benchmarks as well as fine-grained policy enforcement on real-world iOS applications

Until recently, it was widely believed that code randomization (such as fine-grained ASLR) can effectively mitigate code reuse attacks. However, a recent attack strategy, dubbed just-in-time return oriented programming (JIT-ROP), circumvents code randomization by disclosing the (randomized) content of many memory pages at runtime. In order to remedy this situation, new and improved code randomization defenses have been proposed.

The contribution of this paper is twofold: first, we conduct a security analysis of a recently proposed fine-grained ASLR scheme that aims at mitigating JIT-ROP based on hiding direct code references in branch instructions. In particular, we demonstrate its weaknesses by constructing a novel JIT-ROP attack that is solely based on exploiting code references residing on the stack and heap. Our attack stresses that designing code randomization schemes resilient to memory disclosure is highly challenging. Second, we present a new and hybrid defense approach, dubbed Isomeron, that combines code randomization with execution-path randomization to mitigate conventional ROP and JIT-ROP attacks. Our reference implementation of Isomeron neither requires source code nor a static analysis phase.

We evaluated its efficiency based on SPEC benchmarks and discuss its effectiveness against various kinds of code reuse attacks.

Exploitation of memory-corruption vulnerabilities in widely-used software has been a threat for over two decades and no end seems to be in sight. Since performance and backwards compatibility trump security concerns, popular programs such as web browsers, servers, and office suites still contain large amounts of untrusted legacy code written in error-prone languages such as C and C++. At the same time, modern exploits are evolving quickly and routinely incorporate sophisticated techniques such as code reuse and memory disclosure. As a result, they bypass all widely deployed countermeasures including data execution prevention (DEP) and code randomization such as address space layout randomization (ASLR).

The good news is that the security community has recently introduced several promising prototype defenses that offer a more principled response to modern exploits. Even though these solutions have improved substantially over time, they are not perfect and weaknesses that allow bypasses are continually being discovered. Moreover, it remains to be seen whether these prototype defenses can be matured and integrated into operating systems, compilers, and other systems software.

This paper provides a brief overview of current state-of-the-art exploitation and defense techniques against run-time exploits and elaborates on innovative research prototypes that may one day stem the tide of sophisticated exploits. We also provide a brief analysis and categorization of existing defensive techniques and ongoing work in the areas of code randomization and control-flow integrity, and cover both hardware and software-based solutions.

Return-oriented programming (ROP) offers a robust attack technique that has, not surprisingly, been extensively used to exploit bugs in modern software programs (e.g., web browsers and PDF readers). ROP attacks require no code injection, and have already been shown to be powerful enough to bypass fine-grained memory randomization (ASLR) defenses. To counter this ingenious attack strategy, several proposals for enforcement of (coarse-grained) control-flow integrity (CFI) have emerged. The key argument put forth by these works is that coarse-grained CFI policies are sufficient to prevent ROP attacks. As this reasoning has gained traction, ideas put forth in these proposals have even been incorporated into coarse-grained CFI defenses in widely adopted tools (e.g., Microsoft’s EMET framework).

In this paper, we provide the first comprehensive security analysis of various CFI solutions (covering kBouncer, ROPecker, CFI for COTS binaries, ROPGuard, and Microsoft EMET 4.1). A key contribution is in demonstrating that these techniques can be effectively undermined, even under weak adversarial assumptions. More specifically, we show that with bare minimum assumptions, turing-complete and real-world ROP attacks can still be launched even when the strictest of enforcement policies is in use. To do so, we introduce several new ROP attack primitives, and demonstrate the practicality of our approach by transforming existing real-world exploits into more stealthy attacks that bypass coarse-grained CFI defenses.

Return-oriented Programming (ROP) is a powerful exploitation technique used in nearly every exploit today. It maliciously combines short code snippets (gadgets) residing in shared libraries and the executable to bypass data execution prevention (DEP). As a consequence, several new control-flow integrity (CFI) mechanisms and tools have been recently proposed to thwart ROP attacks. For instance, kBouncer and ROPGuard both restrict return instructions to target a call-preceded instruction. In addition, ROPecker and kBouncer force the adversary to invoke a long instruction sequence after a pre-defined number of short gadgets thereby preventing an attacker to execute a sequence of ROP gadgets. Some of the proposed mechanisms have already been integrated in Microsoft's Windows EMET tool. In general, these mechanisms significantly reduce the gadget space and make it challenging for an attacker to mount ROP attacks.

While others have hypothesized or even exploited weaknesses in some of these emerging CFI techniques, we provide the first comprehensive analysis thereof. Specifically, we conducted a security analysis of various recently proposed CFI solutions (including kBouncer, ROPGuard, ROPecker, and CFI for COTS binaries). Our key contribution is in demonstrating that these techniques can be effectively undermined even when all their protection mechanisms are combined. In particular, we transformed existing (publicly available) exploits against Windows (which are detected by Microsoft EMET) into more stealthy attacks that bypass all recently proposed CFI techniques. We show that our performed transformations require no specific assumptions, and demonstrate that a 1MB Windows library (kernel32.dll) is already sufficient to derive a (turing-) complete gadget set using only call-preceded gadgets.

Embedded systems have become pervasive and are built into a vast number of devices such as sensors, vehicles, mobile and wearable devices. However, due to resource constraints, they fail to provide sufficient security, and are particularly vulnerable to runtime attacks (code injection and ROP). Previous works have proposed the enforcement of control-flow integrity (CFI) as a general defense against runtime attacks. However, existing solutions either suffer from performance overhead or only enforce coarse-grain CFI policies that a sophisticated adversary can undermine. In this paper, we tackle these limitations and present the design of novel security hardware mechanisms to enable fine-grained CFI checks. Our CFI proposal is based on a state model and a per-function CFI label approach. In particular, our CFI policies ensure that function returns can only transfer control to active call sides (i.e., return landing pads of functions currently executing). Further, we restrict indirect calls to target the beginning of a function, and lastly, deploy behavioral heuristics for indirect jumps.

Return-oriented programming (ROP) offers a robust attack technique that has, not surprisingly, been extensively used to exploit bugs in modern software programs (e.g., web browsers and PDF readers). ROP attacks require no code injection, and have already been shown to be powerful enough to bypass fine-grained memory randomization (ASLR) defenses. To counter this ingenious attack strategy, several proposals for enforcement of (coarse-grained) control-flow integrity (CFI) have emerged. The key argument put forth by these works is that coarse-grained CFI policies are sufficient to prevent ROP attacks. As this reasoning has gained traction, ideas put forth in these proposals have even been incorporated into coarse-grained CFI defenses in widely adopted tools (e.g., Microsoft’s EMET framework).

In this paper, we provide the first comprehensive security analysis of various CFI solutions (covering kBouncer, ROPecker, CFI for COTS binaries, ROPGuard, and Microsoft EMET 4.1). A key contribution is in demonstrating that these techniques can be effectively undermined, even under weak adversarial assumptions. More specifically, we show that with bare minimum assumptions, turing-complete and real-world ROP attacks can still be launched even when the strictest of enforcement policies is in use. To do so, we introduce several new ROP attack primitives, and demonstrate the practicality of our approach by transforming existing real-world exploits into more stealthy attacks that bypass coarse-grained CFI defenses.

Recently, mobile security has garnered considerable interest in both the research community and industry due to the popularity of smartphones. The current smartphone platforms are open systems that allow application development, also for malicious parties. To protect the mobile device, its user, and other mobile ecosystem stakeholders such as network operators, application execution is controlled by a platform security architecture. This book explores how such mobile platform security architectures work. We present a generic model for mobile platform security architectures: the model illustrates commonly used security mechanisms and techniques in mobile devices and allows a systematic comparison of different platforms. We analyze several mobile platforms using the model. In addition, this book explains hardware-security mechanisms typically present in a mobile device. We also discuss enterprise security extensions for mobile platforms and survey recent research in the area of mobile platform security. The objective of this book is to provide a comprehensive overview of the current status of mobile platform security for students, researchers, and practitioners.

Return-oriented programming ROP offers a powerful technique for undermining state-of-the-art security mechanisms, including non-executable memory and address space layout randomization. To mitigate this daunting attack strategy, several in-built defensive mechanisms have been proposed. In this work, we instead focus on detection techniques that do not require any modification to end-user platforms. Specifically, we propose a novel framework that efficiently analyzes documents PDF, Office, or HTML files and detects whether they contain a returnoriented programming payload. To do so, we provide advanced techniques for taking memory snapshots of a target application, efficiently transferring the snapshots to a host system, as well as novel static analysis and filtering techniques to identify and profile chains of code pointers referencing ROP gadgets that may even reside in randomized libraries. Our evaluation of over 7,662 benign and 57 malicious documents demonstrate that we can perform such analysis accurately and expeditiously -- with the vast majority of documents analyzed in about 3 seconds.

Fine-grained address space layout randomization (ASLR) has recently been proposed as a method of efficiently mitigating runtime attacks. In this presentation, we introduce the design and implementation of a framework based on a novel attack strategy, dubbed just-in-time code reuse, which both undermines the benefits of fine-grained ASLR and greatly enhances the ease of exploit development on today’s platforms that combine standard ASLR and DEP (e.g. Windows 8). Specifically, we derail the assumptions embodied in fine-grained ASLR by exploiting the ability to repeatedly abuse a memory disclosure to map an application’s memory layout on-the-fly, dynamically discover API functions and gadgets, and JIT-compile a target program using those gadgets - all within a script environment at the time an exploit is launched. We demonstrate the power of our framework by using it in conjunction with a real-world exploit against Internet Explorer, show its effectiveness in Windows 8, and also provide extensive evaluations that demonstrate the practicality of just-in-time code reuse attacks. Our findings suggest that fine-grained ASLR may not be as promising as first thought.

Code reuse attacks such as return-oriented programming are one of the most powerful threats to contemporary software. ASLR was introduced to impede these attacks by dispersing shared libraries and the executable in memory. However, in practice its entropy is rather low and, more importantly, the leakage of a single address reveals the position of a whole library in memory. The recent mitigation literature followed the route of randomization, applied it at different stages such as source code or the executable binary. However, the code segments still stay in one block. In contrast to previous work, our randomization solution, called XIFER, (1) disperses all code (executable and libraries) across the whole address space, (2) re-randomizes the address space for each run, (3) is compatible to code signing, and (4) does neither require offline static analysis nor source-code. Our prototype implementation supports the Linux ELF file format and covers both mainstream processor architectures x86 and ARM. Our evaluation demonstrates that XIFER performs efficiently at load- and during run-time (1.2% overhead).

Fine-grained address space layout randomization (ASLR) has recently been proposed as a method of efficiently mitigating runtime attacks. In this paper, we introduce the design and implementation of a framework based on a novel attack strategy, dubbed just-in-time code reuse, that undermines the benefits of fine-grained ASLR. Specifically, we derail the assumptions embodied in fine-grained ASLR by exploiting the ability to repeatedly abuse a memory disclosure to map an application’s memory layout on-the-fly, dynamically discover API functions and gadgets, and JIT-compile a target program using those gadgets - all within a script environment at the time an exploit is launched. We demonstrate the power of our framework by using it in conjunction with a real-world exploit against Internet Explorer, and also provide extensive evaluations that demonstrate the practicality of just-in-time code reuse attacks. Our findings suggest that fine-grained ASLR may not be as promising as first thought.

Apple iOS is one of the most popular mobile operating systems. As its core security technology, iOS provides application sandboxing but assigns a generic sandboxing profile to every third-party application. However, recent attacks and incidents with benign applications demonstrate that this design decision is vulnerable to crucial privacy and security breaches, allowing applications (either benign or malicious) to access contacts, photos, and device IDs. Moreover, the dynamic character of iOS apps written in Objective-C renders the currently proposed static analysis tools less useful.

In this paper, we aim to address the open problem of preventing (not only detecting) privacy leaks and simultaneously strengthening security against runtime attacks on iOS. Compared to similar research work on the open Android, realizing such a system for the closed-source iOS is highly involved.

We present the design and implementation of PSiOS, a tool that features a novel policy enforcement framework for iOS. It provides fine-grained, application-specific, and user/administrator defined sandboxing for each third-party application without requiring access to the application source code. Our reference implementation deploys control-flow integrity based on the recently proposed MoCFI (Mobile CFI) framework that only protects applications against runtime attacks. We evaluated several popular iOS applications (e.g., Facebook, WhatsApp) to demonstrate the efficiency and effectiveness of PSiOS.

The enormous growth of mobile devices and their app markets has raised many security and privacy concerns. Runtime attacks seem to be a major threat, in particular, code-reuse attacks that do not require any external code injection (e.g., return-to-libc or return-oriented programming).

We present, for the first time, a code transformation tool that completely mitigates code-reuse attacks by applying software diversity to the binary at runtime. Our tool XIFER (1) randomly diversifies the code of an application over the entire memory for each invocation, (2) requires no source code or any static analysis, (3) can be applied to both Intel x86 and ARM Linux executables, and (4) induces a negligible runtime overhead of only 1% in average.

Android’s security framework has been an appealing subject of research in the last few years. Android has been shown to be vulnerable to application-level privilege escalation attacks, such as confused deputy attacks, and more recently, attacks by colluding applications. While most of the proposed approaches aim at solving confused deputy attacks, there is still no solution that simultaneously addresses collusion attacks.

In this paper, we investigate the problem of designing and implementing a practical security framework for Android to protect against confused deputy and collusion attacks. We realize that defeating collusion attacks calls for a rather system-centric solution as opposed to application-dependent policy enforcement. To support our design decisions, we conduct a heuristic analysis of Android’s system behavior (with popular apps) to identify attack patterns, classify different adversary models, and point out the challenges to be tackled. Then we propose a solution for a system-centric and policy-driven runtime monitoring of communication channels between applications at multiple layers: 1) at the middleware we control IPCs between applications and indirect communication via Android system components. Moreover, inspired by the approach in QUIRE, we establish semantic links between IPCs and enable the reference monitor to verify the call-chain; 2) at the kernel level we realize mandatory access control on the file system (including Unix domain sockets) and local Internet sockets. To allow for runtime, dynamic low-level policy enforcement, we provide a callback channel between the kernel and the middleware. Finally, we evaluate the efficiency and effectiveness of our framework on known confused deputy and collusion attacks, and discuss future directions.

Runtime and control-flow attacks (such as code injection or return-oriented programming) constitute one of the most severe threats to software programs. These attacks are prevalent and have been recently applied to smartphone applications as well, of which hundreds of thousands are downloaded by users every day. While a framework for control-flow integrity (CFI) enforcement, an approach to prohibit this kind of attacks, exists for the Intel x86 platform, there is no such a solution for smartphones.

In this paper, we present a novel framework, MoCFI (Mobile CFI), that provides a general countermeasure against control-flow attacks on smartphone platforms by enforcing CFI. We show that CFI on typical smartphone platforms powered by an ARM processor is technically involved due to architectural differences between ARM and Intel x86, as well as the specifics of smartphone OSes. Our framework performs CFI on-the-fly during runtime without requiring the application’s source code. For our reference implementation we chose Apple’s iOS, because it has been an attractive target for control-flow attacks. Nevertheless, our framework is also applicable to other ARM-based devices such as Google’s Android. Our performance evaluation demonstrates that MoCFI is efficient and does not induce notable overhead when applied to popular iOS applications.

In this paper we present the design and implementation of a security framework that extends the reference monitor of the Android middleware and deploys a mandatory access control on Linux kernel (based on Tomoyo) aiming at detecting and preventing application-level privilege escalation attacks at runtime.

In contrast to existing solutions, our framework is system-centric, efficient, detects attacks that involve communication channels controlled by both, Android middleware and the Linux kernel (particularly, Binder IPC, Internet sockets and file system). It can prevent known confused deputy attacks without false positives and is also flexible enough to prevent unknown confused deputy attacks and attacks by colluding applications (e.g., Soundcomber) at the cost of a small rate of false positives.

In this paper, we introduce a security framework for practical and lightweight domain isolation on Android to mitigate unauthorized data access and communication among applications of different trust levels (e.g., private and corporate). We present the design and implementation of our framework, TrustDroid, which in contrast to existing solutions enables isolation at different layers of the Android software stack: (1) at the middleware layer to prevent inter-domain application communication and data access, (2) at the kernel layer to enforce mandatory access control on the file system and on Inter-Process Communication (IPC) channels, and (3) at the network layer to mediate network traffic. For instance, (3) allows network data to be only read by a particular domain, or enables basic context-based policies such as preventing Internet access by untrusted applications while an employee is connected to the company’s network.

Our approach accurately addresses the demands of the business world, namely to isolate data and applications of different trust levels in a practical and lightweight way. Moreover, our solution is the first leveraging mandatory access control with TOMOYO Linux on a real Android device (Nexus One). Our evaluation demonstrates that TrustDroid only adds a negligible overhead, and in contrast to contemporary full virtualization, only minimally affects the battery’s life-time.

Despite extensive research over the last two decades, runtime attacks on software are still prevalent. Recently, smartphones, of which millions are in use today, have become an attractive target for adversaries. However, existing solutions are either ad-hoc or limited in their effectiveness.

In this poster, we present a general countermeasure against runtime attacks on smartphone platforms. Our approach makes use of control-flow integrity (CFI), and tackles unique challenges of the ARM architecture and smartphone platforms. Our framework and implementation is efficient, since it requires no access to source code, performs CFI enforcement on-the-fly during runtime, and is compatible to memory randomization and code signing/encryption. We chose Apple iPhone for our reference implementation, because it has become an attractive target for runtime attacks. Our performance evaluation on a real iOS device demonstrates that our implementation does not induce any notable overhead when applied to popular iOS applications.

Google Android has become a popular mobile operating system which is increasingly deployed by mobile device manufactures for various platforms. Recent attacks show that Android’s permission framework is vulnerable to application-level privilege escalation attacks, i.e., an application may indirectly gain privileges to perform unauthorized actions. The existing proposals for security extensions to Android’s middleware (e.g., Kirin, Saint or TaintDroid) cannot fully and adequately mitigate these attacks or detect Trojans such as Soundminer that exploit covert channels in the Android system.

In this paper we present the design and implementation of XManDroid (eXtended Monitoring on Android), a security framework that extends the monitoring mechanism of Android to detect and prevent application-level privilege escalation attacks at runtime based on a system-centric system policy. Our implementation dynamically analyzes applications’ transitive permission usage while inducing a minimal performance overhead unnoticeable for the user. Depending on system policy our system representation allows for an effective detection of (covert) channels established through the Android system services and content providers while simultaneously optimizing the rate of false positives. We evaluate the effectiveness of XManDroid on our test suite that simulates known application-level privilege escalation attacks (including Soundminer), and demonstrate successful detection of attacks that use Android’s inter-component communication (ICC) framework (standard for most attacks). We also preformed a usability test to evaluate the impact of XManDroid on the user-experience with third party applications. Moreover, we analyze sources of false positives and discuss how this rate can be further significantly reduced.

Modern runtime attacks increasingly make use of the powerful return-oriented programming (ROP) attack techniques and principles such as recent attacks on Apple iPhone and Acrobat products to name some. These attacks even work under the presence of modern memory protection mechanisms such as data execution prevention (DEP). In this paper, we present our tool, ROPdefender, that dynamically detects conventional ROP attacks (that are based on return instructions). In contrast to existing solutions, ROPdefender can be immediately deployed by end-users, since it does not rely on side information (e.g., source code or debugging information) which are rarely provided in practice. Currently, our tool adds a runtime overhead of 2x which is comparable to similar instrumentation-based tools.

Android is a modern and popular software platform for smartphones. Among its predominant features is an advanced security model which is based on application-oriented mandatory access control and sandboxing. This allows developers and users to restrict the execution of an application to the privileges it has (mandatorily) assigned at installation time. The exploitation of vulnerabilities in program code is hence believed to be confined within the privilege boundaries of an application’s sandbox. However, in this paper we show that a privilege escalation attack is possible. We show that a genuine application exploited at runtime or a malicious application can escalate granted permissions. Our results immediately imply that Android’s security model cannot deal with a transitive permission usage attack and Android’s sandbox model fails as a last resort against malware and sophisticated runtime attacks.

In this paper we present a novel and general memory-related attack method on ARM-based computing platforms. Our attack deploys the principles of return-oriented programming (ROP), however, in contrast to conventional ROP, it exploits jumps instead of returns, and hence it can not be detected by return address checkers. Although a similar attack has been recently proposed for Intel x86, it was unclear if the attack technique can be deployed to ARM-based computing platforms as well. Developing a jump-based attack on ARM is more involved, because ARM is based on a RSIC architecture which differs in many aspects from Intel's x86 architecture. Nevertheless, we show a Turing-complete attack that can induce arbitrary change of behavior in running programs without requiring code injection. As proof of concept, we instantiate our attack method on the Android platform.

Return-oriented programming (ROP) is a technique that enables an adversary to construct malicious programs with the desired behavior by combining short instruction sequences that already reside in the memory space of a program. ROP attacks have already been demonstrated on various processor architectures ranging from PCs to smartphones and special-purpose systems.

In this paper, we present our tool, ROPdefender, that dynamically detects conventional ROP attacks (that are based on return instructions) with a reasonable runtime overhead of 2x. In contrast to existing solutions, (i) ROPdefender does not rely on side information (e.g., source code or debugging information) and (ii) it instruments all return instructions issued during program execution including all returns from dynamic libraries, even if the adversary subverts the control flow by other means. Moreover, ROPdefender can handle Unix signals, non-local control transfers, C++ exceptions, lazy binding, and can be applied to multi-threaded applications such as Mozilla Firefox or Acrobat Reader. Finally our implementation supports mainstream operating systems (Windows and Linux) for the Intel x86 architecture. As proof of concept we show that ROPdefender successfully detects recent Acrobat Reader exploits on Windows.

Despite the many efforts made in recent years to mitigate runtime attacks such as stack and heap based buffer overflows, these attacks are still a common security concern in today's computing platforms. Attackers have even found new ways to enforce runtime attacks including use of a technique called return-oriented programming. Trusted Computing provides mechanisms to verify the integrity of all executable content in an operating system. But they only provide integrity at load-time and are not able to prevent or detect runtime attacks. To mitigate return-oriented programming attacks, we propose new runtime integrity monitoring techniques that use tracking instrumentation of program binaries based on taint analysis and dynamic tracing. We also describe how these techniques can be employed in a dynamic integrity measurement architecture (DynIMA). In this way we fill the gap between static load-time and dynamic runtime attestation and, in particular, extend trusted computing techniques to effectively defend against return-oriented programming attacks.