Forged Transactions: Securing Smart Contracts Against Signature Reuse

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Author: Denis Avetisyan

A new analysis reveals how attackers can exploit signature replay vulnerabilities in smart contracts, and introduces a system for automatically detecting these critical flaws.

The system demonstrates a vulnerability wherein a previously captured, legitimate signature can be replayed to gain unauthorized access, highlighting the inherent fragility of authentication protocols reliant solely on signal reception without considering temporal validity or source verification.
The system demonstrates a vulnerability wherein a previously captured, legitimate signature can be replayed to gain unauthorized access, highlighting the inherent fragility of authentication protocols reliant solely on signal reception without considering temporal validity or source verification.

This work presents LASiR, a system combining Large Language Models with static and symbolic analysis to identify smart contract vulnerabilities affecting assets valued at $4.76 million.

While smart contracts enhance blockchain security through digital signatures, a lack of robust usage checks can inadvertently create opportunities for malicious actors. This issue is explored in ‘One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts’, which details a prevalent vulnerability where signatures are reused inappropriately, potentially compromising contract assets. Our research reveals that such Signature Replay Vulnerabilities (SRVs) are widespread, affecting contracts holding $4.76 million across major blockchains, with nearly 20% of Ethereum contracts using signatures being vulnerable. Can automated detection systems, leveraging the power of Large Language Models and advanced static/symbolic analysis, effectively mitigate this growing threat to smart contract security?

The Inevitable Surface: Smart Contract Security

Smart contracts, the foundation of decentralized applications, now manage billions in digital assets, making them prime targets. Increasing financial incentives demand advanced security beyond conventional approaches. Traditional paradigms struggle with the complexity and rapid evolution of smart contract code; static analysis produces false positives, and dynamic analysis lacks comprehensive test coverage. Ensuring the integrity of digital signature schemes—particularly the Ecrecover Function—is paramount, as these underpin trust within the decentralized ecosystem. Sometimes, understanding inherent fragility is more valuable than pursuing the illusion of permanence.

First Lines of Defense: Static Analysis Techniques

Static analysis, including Static Taint Analysis, is crucial for identifying potential vulnerabilities without code execution. These methods proactively audit source code for flaws like buffer overflows and injection vulnerabilities. Frameworks like Slither provide a robust foundation for smart contract security, detecting common Solidity vulnerabilities with a user-friendly interface. However, current implementations struggle with complex interactions and novel attack vectors. Combining static analysis with techniques like symbolic execution and formal verification, potentially augmented by machine learning for anomaly detection, is essential to mitigate emerging risks.

LASiR: Intelligent Verification for a Complex Landscape

The LASiR tool integrates Large Language Models (LLMs) with Static Taint Analysis and Symbolic Execution to enhance the detection of Security-Relevant Vulnerabilities (SRVs) within smart contracts. This combined methodology addresses limitations of both traditional static analysis and emerging LLM approaches. Evaluations demonstrate that LASiR achieves 95.83% recall and 88.46% F1-score in identifying Signature Replay Vulnerabilities (SRVs), a substantial improvement in complex contract environments. The tool’s ability to correlate symbolic execution with LLM-derived semantic understanding enables comprehensive vulnerability assessment.

Analysis utilizing large language models reveals key insights into the Interest Protocol.
Analysis utilizing large language models reveals key insights into the Interest Protocol.

Practical application of LASiR has identified vulnerabilities impacting $4.76 million in active assets, underscoring its effectiveness in real-world scenarios. This synergistic approach is pivotal in handling complex contracts where vulnerabilities stem from intricate interactions and require deeper semantic understanding.

GPTScan: Augmenting Static Verification with LLMs

GPTScan represents a novel approach to vulnerability detection in LLMs and smart contracts, integrating LLM-based analysis with established static verification techniques. This hybrid methodology leverages the reasoning capabilities of LLMs to improve the precision of static analysis. A key component is its extension of Static Taint Analysis, substantially improving the ability to pinpoint and rank critical security vulnerabilities. Analysis employing GPTScan on 918,964 Ethereum contracts through the LASiR framework revealed that 19.63% exhibited Signature Replay Vulnerabilities, highlighting their prevalence.

The convergence of LLMs and static verification holds significant implications for smart contract security. Automating and enhancing vulnerability discovery promises a proactive, scalable approach to safeguarding decentralized applications. Stability is an illusion cached by time, and tools like GPTScan offer a fleeting moment of certainty in the relentless flow of potential exploits.

The pursuit of secure smart contracts, as demonstrated by LASiR, echoes a fundamental truth about complex systems. Every commit represents a record in the annals of blockchain history, and each version a chapter in its evolution. Henri PoincarĂ© observed, “Mathematics is the art of giving reasons, even in matters of taste.” This sentiment applies directly to vulnerability detection; LASiR doesn’t merely flag potential issues, it reasons through the code, utilizing LLMs and analysis techniques to provide justifications for its findings. The system’s ability to identify affected assets—totaling $4.76 million—highlights the tangible consequences when the reasoning falters, or when assumptions about contract behavior prove incorrect. Delaying fixes, in this context, is a tax on ambition, a price paid for neglecting the foundational logic upon which these systems depend.

What Lies Ahead?

The identification of $4.76 million in potentially recoverable assets, as demonstrated by LASiR, is less a triumph and more an accounting of accrued technical debt. Each detected vulnerability isn’t a failure of current tooling, but a predictable consequence of complex systems operating within a trust-minimized environment. Time, as always, reveals the fault lines. The system doesn’t prevent replay attacks; it simply highlights where past implementations have conceded ground to the inevitable entropy of state.

Future work will undoubtedly focus on automating remediation—treating symptoms rather than addressing the underlying condition of imperfect code. However, a more fruitful, if less immediately profitable, avenue lies in reframing the problem. Instead of seeking to eliminate replay vulnerabilities, perhaps the focus should shift to managing them—designing contracts that gracefully degrade in the event of such incidents, minimizing damage and maximizing recovery. Such an approach accepts the reality of system decay as a feature, not a bug.

The integration of Large Language Models, while promising, presents its own limitations. LASiR’s efficacy is tied to the training data and the models’ inherent biases. The true test won’t be identifying known patterns, but anticipating novel attack vectors—a task that demands more than pattern recognition, and requires a deeper understanding of systemic risk. The system will evolve, and so too must the methods for assessing its resilience—not as a matter of prevention, but of managed decline.

Original article: https://arxiv.org/pdf/2511.09134.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-13 21:06