arXiv subjects

Ghassan Karame

Publications and source records attributed to Ghassan Karame.

At least 19 recordsLinked to original sources

arXiv2026

"They don't care about this": A Systematic Study of TEE Build Reproducibility in the Wild

Trusted Execution Environments (TEEs) have become a cornerstone of modern cloud computing, providing strong confidentiality and integrity guarantees for both code and data. A critical component of this trust model is remote attestation, which enables external entities to verify the authenticity and integrity of code executing within a TEE through cryptographic measurements. However, the effectiveness of remote attestation fundamentally depends on the verifier's ability to trace the reported measurement back to the original source code - a property that can only be guaranteed through reproducible builds. In this paper, we investigate the reproducibility of TEE builds through a technical analysis of 115 TEE deployments. Our analysis spans popular TEEs such as Intel SGX, Intel TDX, and AMD SEV, and reveals that a striking 91% of those deployments were not reproducible, with 80% failing to provide both source code and a reference build, the two essential prerequisites for reproducibility. To explore the root causes, we contacted the maintainers of 50 SGX projects and managed to recruit 12 developers from industry and academia for interviews. Only one of our participants reported that reproducibility is a priority during development, effectively confirming our technical findings. Beyond technical barriers (e.g., timestamps included in the binary) that can be readily addressed, we identify broader ecosystem-level challenges, such as the lack of control over the build environment in projects involving multiple stakeholders. We argue that achieving reproducibility in TEEs requires a holistic development approach that extends beyond individual developers and calls for stronger commitments - rather than treating TEEs as a "security badge".

cs.CR↗

arXiv2026

On Identifying Sound Conditions for Frontrunning Resistance

Blockchains enable decentralized applications through smart contracts---interactive programs executed through consensus. However, the inherently asynchronous nature of blockchain transaction ordering introduces a class of vulnerabilities known as frontrunning attacks, which have caused millions of dollars in losses in major blockchains, such as Ethereum. Frontrunning attacks arise because users interact with smart contracts through transactions, which are added to the blockchain by designated nodes called miners. Miners can exploit their ability to reorder, delay, or insert transactions to gain an advantage over honest users, effectively frontrunning them. Yet, to date, the field lacks a rigorous definition of what it even means for a contract to resist such attacks. Worse, we show that existing dynamic detection approaches are fundamentally inadequate: in a large-scale study comprising 287 smart contract audits, 55% of the 393 reported vulnerabilities identified by leading smart contract auditors fall outside the scope of state-of-the-art detection criteria. To address this gap, we propose the first formal definition of frontrunning vulnerability for smart contracts. Our definition captures a key insight: resistance to frontrunning is not an intrinsic property of a contract alone, but depends critically on how honest users interact with it. Grounded in this observation, we develop a sound algorithm for synthesizing secure interaction conditions, alongside a prototype implementation that we apply to audited real-world contracts---revealing previously undiscovered vulnerabilities in two Ethereum contracts.

cs.CR↗

arXiv2026

On Securing the Software Development Lifecycle in IoT RISC-V Trusted Execution Environments

RISC-V-based Trusted Execution Environments (TEEs) are gaining traction in the automotive and IoT sectors as a foundation for protecting sensitive computations. However, the supporting infrastructure around these TEEs remains immature. In particular, mechanisms for secure enclave updates and migrations - essential for complete enclave lifecycle management - are largely absent from the evolving RISC-V ecosystem. In this paper, we address this limitation by introducing a novel toolkit that enables RISC-V TEEs to support critical aspects of the software development lifecycle. Our toolkit provides broad compatibility with existing and emerging RISC-V TEE implementations (e.g., Keystone and CURE), which are particularly promising for integration in the automotive industry. It extends the Security Monitor (SM) - the trusted firmware layer of RISC-V TEEs - with three modular extensions that enable secure enclave update, secure migration, state continuity, and trusted time. Our implementation demonstrates that the toolkit requires only minimal interface adaptation to accommodate TEE-specific naming conventions. Our evaluation results confirm that our proposal introduces negligible performance overhead: our state continuity solution incurs less than 1.5% overhead, and enclave downtime remains as low as 0.8% for realistic applications with a 1 KB state, which conforms with the requirements of most IoT and automotive applications.

cs.CR↗

arXiv2026

SseRex: Practical Symbolic Execution of Solana Smart Contracts

Solana is rapidly gaining traction among smart contract developers and users. However, its growing adoption has been accompanied by a series of major security incidents, which have spurred research into automated analysis techniques for Solana smart contracts. Unfortunately, existing approaches do not address the unique and complex account model of Solana. In this paper, we propose SseRex, the first symbolic execution vulnerability detection approach for finding Solana-specific bugs such as missing owner checks, missing signer checks, and missing key checks, as well as arbitrary cross-program invocations. Our evaluation of 8,714 bytecode-only contracts shows that our approach outperforms existing approaches and identifies potential bugs in 467 different contracts. Additionally, we analyzed 120 open-source Solana projects and conducted in-depth case studies on four of them. Our findings reveal that subtle, easily overlooked issues often serve as the root cause of severe exploits, further highlighting the need for specialized analysis tools like SseRex.

cs.CR↗

arXiv2026

Mitigating Collusion in Proofs of Liabilities

Cryptocurrency exchanges use proofs of liabilities (PoLs) to prove to their customers their liabilities committed on-chain, thereby enhancing their trust in the service. Unfortunately, a close examination of currently deployed and academic PoLs reveals significant shortcomings in their designs. For instance, existing schemes cannot resist realistic attack scenarios in which the provider colludes with an existing user. In this paper, we propose a new model, dubbed permissioned PoL, that addresses this gap by not requiring cooperation from users to detect a dishonest provider's potential misbehavior. At the core of our proposal lies a novel primitive, which we call Permissioned Vector Commitment (PVC), to ensure that a committed vector only contains values that users have explicitly signed. We provide an efficient PVC and PoL construction that carefully combines homomorphic properties of KZG commitments and BLS-based signatures. Our prototype implementation shows that, despite the stronger security, our proposal also improves server performance (by up to $10\times$) compared to prior PoLs.

cs.CR↗

arXiv2026

On the Effectiveness of Mempool-based Transaction Auditing

While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.

cs.CR↗

arXiv2026

On Abnormal Execution Timing of Conditional Jump Instructions

An extensive line of work on modern computing architectures has shown that the execution time of instructions can (i) depend on the operand of the instruction or (ii) be influenced by system optimizations, e.g., branch prediction and speculative execution paradigms. In this paper, we systematically measure and analyze timing variabilities in conditional jump instructions that can be macro-fused with a preceding instruction, depending on their placement within the binary. Our measurements indicate that these timing variations stem from the micro-op cache placement and the jump's offset in the L1 instruction cache of modern processors. We demonstrate that this behavior is consistent across multiple microarchitectures, including Skylake, Coffee Lake, and Kaby Lake, as well as various real-world implementations. We confirm the prevalence of this variability through extensive experiments on a large-scale set of popular binaries, including libraries from Ubuntu 24.04, Windows 10 Pro, and several open-source cryptographic libraries. We also show that one can easily avoid this timing variability by ensuring that macro-fusible instructions are 32-byte aligned - an approach initially suggested in 2019 by Intel in an overlooked short report. We quantify the performance impact of this approach across the cryptographic libraries, showing a speedup of 2.15% on average (and up to 10.54%) when avoiding the timing variability. As a by-product, we show that this variability can be exploited as a covert channel, achieving a maximum throughput of 16.14 Mbps.

cs.CR↗

arXiv2026

The Real Menace of Cloning Attacks on SGX Applications

Trusted Execution Environments (TEEs) are gaining popularity as an effective means to provide confidentiality in the cloud. TEEs, such as Intel SGX, suffer from so-called rollback and cloning attacks (often referred to as forking attacks). Rollback attacks are enabled by the lack of freshness guarantees for sealed data; cloning attacks stem from the inability to determine if other instances of an enclave are running on the same platform. While rollback attacks have been extensively studied by the community, cloning attacks have been, unfortunately, less investigated. To address this gap, we extensively study and thoroughly analyze the susceptibility of 72 SGX-based proposals to cloning attacks. Our results show that roughly 20% of the analyzed proposals are insecure against cloning attacks-including those applications that rely on monotonic counters and are, therefore, secure against rollback attacks.

cs.CR↗

arXiv2025

Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks using Hyperparameter Tuning

In this paper, we present the first detailed analysis of how training hyperparameters -- such as learning rate, weight decay, momentum, and batch size -- influence robustness against both transfer-based and query-based attacks. Supported by theory and experiments, our study spans a variety of practical deployment settings, including centralized training, ensemble learning, and distributed training. We uncover a striking dichotomy: for transfer-based attacks, decreasing the learning rate significantly enhances robustness by up to $64\%$. In contrast, for query-based attacks, increasing the learning rate consistently leads to improved robustness by up to $28\%$ across various settings and data distributions. Leveraging these findings, we explore -- for the first time -- the training hyperparameter space to jointly enhance robustness against both transfer-based and query-based attacks. Our results reveal that distributed models benefit the most from hyperparameter tuning, achieving a remarkable tradeoff by simultaneously mitigating both attack types more effectively than other training setups.

cs.LG↗

arXiv2025

On Hyperparameters and Backdoor-Resistance in Horizontal Federated Learning

Horizontal Federated Learning (HFL) is particularly vulnerable to backdoor attacks as adversaries can easily manipulate both the training data and processes to execute sophisticated attacks. In this work, we study the impact of training hyperparameters on the effectiveness of backdoor attacks and defenses in HFL. More specifically, we show both analytically and by means of measurements that the choice of hyperparameters by benign clients does not only influence model accuracy but also significantly impacts backdoor attack success. This stands in sharp contrast with the multitude of contributions in the area of HFL security, which often rely on custom ad-hoc hyperparameter choices for benign clients$\unicode{x2013}$leading to more pronounced backdoor attack strength and diminished impact of defenses. Our results indicate that properly tuning benign clients' hyperparameters$\unicode{x2013}$such as learning rate, batch size, and number of local epochs$\unicode{x2013}$can significantly curb the effectiveness of backdoor attacks, regardless of the malicious clients' settings. We support this claim with an extensive robustness evaluation of state-of-the-art attack-defense combinations, showing that carefully chosen hyperparameters yield across-the-board improvements in robustness without sacrificing main task accuracy. For example, we show that the 50%-lifespan of the strong A3FL attack can be reduced by 98.6%, respectively$\unicode{x2013}$all without using any defense and while incurring only a 2.9 percentage points drop in clean task accuracy.

cs.CR↗

arXiv2025

Targeted Physical Evasion Attacks in the Near-Infrared Domain

A number of attacks rely on infrared light sources or heat-absorbing material to imperceptibly fool systems into misinterpreting visual input in various image recognition applications. However, almost all existing approaches can only mount untargeted attacks and require heavy optimizations due to the use-case-specific constraints, such as location and shape. In this paper, we propose a novel, stealthy, and cost-effective attack to generate both targeted and untargeted adversarial infrared perturbations. By projecting perturbations from a transparent film onto the target object with an off-the-shelf infrared flashlight, our approach is the first to reliably mount laser-free targeted attacks in the infrared domain. Extensive experiments on traffic signs in the digital and physical domains show that our approach is robust and yields higher attack success rates in various attack scenarios across bright lighting conditions, distances, and angles compared to prior work. Equally important, our attack is highly cost-effective, requiring less than US\$50 and a few tens of seconds for deployment. Finally, we propose a novel segmentation-based detection that thwarts our attack with an F1-score of up to 99%.

cs.CR↗

arXiv2024

On the Robustness of Distributed Machine Learning against Transfer Attacks

Although distributed machine learning (distributed ML) is gaining considerable attention in the community, prior works have independently looked at instances of distributed ML in either the training or the inference phase. No prior work has examined the combined robustness stemming from distributing both the learning and the inference process. In this work, we explore, for the first time, the robustness of distributed ML models that are fully heterogeneous in training data, architecture, scheduler, optimizer, and other model parameters. Supported by theory and extensive experimental validation using CIFAR10 and FashionMNIST, we show that such properly distributed ML instantiations achieve across-the-board improvements in accuracy-robustness tradeoffs against state-of-the-art transfer-based attacks that could otherwise not be realized by current ensemble or federated learning instantiations. For instance, our experiments on CIFAR10 show that for the Common Weakness attack, one of the most powerful state-of-the-art transfer-based attacks, our method improves robust accuracy by up to 40%, with a minimal impact on clean task accuracy.

cs.LG↗

arXiv2024

Fuzz on the Beach: Fuzzing Solana Smart Contracts

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 literature features extensive tooling support for smart contract security, current solutions are largely tailored for the Ethereum Virtual Machine. 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 find bugs 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.

cs.CR↗

arXiv2024

The Forking Way: When TEEs Meet Consensus

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good "marriage": TEEs bring confidential computing to the blockchain while the consensus layer could help defend TEEs from forking attacks. In this paper, we systemize how current blockchain solutions integrate TEEs and to what extent they are secure against forking attacks. To do so, we thoroughly analyze 29 proposals for TEE-based blockchains, ranging from academic proposals to production-ready platforms. We uncover a lack of consensus in the community on how to combine TEEs and blockchains. In particular, we identify four broad means to interconnect TEEs with consensus, analyze their limitations, and discuss possible remedies. Our analysis also reveals previously undocumented forking attacks on three production-ready TEE-based blockchains: Ten, Phala, and the Secret Network. We leverage our analysis to propose effective countermeasures against those vulnerabilities; we responsibly disclosed our findings to the developers of each affected platform.

cs.CR↗

arXiv2024

Practical Light Clients for Committee-Based Blockchains

Light clients are gaining increasing attention in the literature since they obviate the need for users to set up dedicated blockchain full nodes. While the literature features a number of light client instantiations, most light client protocols optimize for long offline phases and implicitly assume that the block headers to be verified are signed by highly dynamic validators. In this paper, we show that (i) most light clients are rarely offline for more than a week, and (ii) validators are unlikely to drastically change in most permissioned blockchains and in a number of permissionless blockchains, such as Cosmos and Polkadot. Motivated by these findings, we propose a novel practical system that optimizes for such realistic assumptions and achieves minimal communication and computational costs for light clients when compared to existing protocols. By means of a prototype implementation of our solution, we show that our protocol achieves a reduction by up to $90$ and $40000\times$ (respectively) in end-to-end latency and up to $1000$ and $10000\times$ (respectively) smaller proof size when compared to two state-of-the-art light client instantiations from the literature.

cs.CR↗

arXiv2024

Defying the Odds: Solana's Unexpected Resilience in Spite of the Security Challenges Faced by Developers

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 consisting of a 90-minute Solana smart contract code review task with 35 participants followed by interviews with a subset of seven participants. 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 aiding Solana developers in deploying secure contracts.

cs.CR↗

arXiv2024

Larger-scale Nakamoto-style Blockchains Don't Necessarily Offer Better Security

Extensive research on Nakamoto-style consensus protocols has shown that network delays degrade the security of these protocols. Established results indicate that, perhaps surprisingly, maximal security is achieved when the network is as small as two nodes due to increased delays in larger networks. This contradicts the very foundation of blockchains, namely that decentralization improves security. In this paper, we take a closer look at how the network scale affects security of Nakamoto-style blockchains. We argue that a crucial aspect has been neglected in existing security models: the larger the network, the harder it is for an attacker to control a significant amount of power. To this end, we introduce a probabilistic corruption model to express the increasing difficulty for an attacker to corrupt resources in larger networks. Based on our model, we analyze the impact of the number of nodes on the (maximum) network delay and the fraction of adversarial power. In particular, we show that (1) increasing the number of nodes eventually violates security, but (2) relying on a small number of nodes does not provide decent security provisions either. We then validate our analysis by means of an empirical evaluation emulating hundreds of thousands of nodes in deployments such as Bitcoin, Monero, Cardano, and Ethereum Classic. Based on our empirical analysis, we concretely analyze the impact of various real-world parameters and configurations on the consistency bounds in existing deployments and on the adversarial power that can be tolerated while providing security. As far as we are aware, this is the first work that analytically and empirically explores the real-world tradeoffs achieved by current popular Nakamoto-style deployments.

cs.CR↗

arXiv2024

Closing the Gap: Achieving Better Accuracy-Robustness Tradeoffs against Query-Based Attacks

Although promising, existing defenses against query-based attacks share a common limitation: they offer increased robustness against attacks at the price of a considerable accuracy drop on clean samples. In this work, we show how to efficiently establish, at test-time, a solid tradeoff between robustness and accuracy when mitigating query-based attacks. Given that these attacks necessarily explore low-confidence regions, our insight is that activating dedicated defenses, such as random noise defense and random image transformations, only for low-confidence inputs is sufficient to prevent them. Our approach is independent of training and supported by theory. We verify the effectiveness of our approach for various existing defenses by conducting extensive experiments on CIFAR-10, CIFAR-100, and ImageNet. Our results confirm that our proposal can indeed enhance these defenses by providing better tradeoffs between robustness and accuracy when compared to state-of-the-art approaches while being completely training-free.

cs.CV↗