The paper conducts an interpretability-driven safety audit of eight state-of-the-art LLMs, demonstrating that while interpretability-based steering is a powerful auditing tool, model robustness varies…

The paper introduces SIGIL, a novel framework that cryptographically seals the entire lifecycle of LLM skills, ensuring verifiable integrity from publication through runtime execution to prevent suppl…

GoAT-X introduces a novel framework that structures cross-chain smart contract auditing as a Graph of Auditing Thoughts, significantly improving the detection of complex, semantic vulnerabilities in m…

The paper introduces SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier models can produce compilab…

The paper introduces SCDBench, a comprehensive benchmark dataset and methodology that rigorously evaluates LLM-based smart contract decompilers, finding that while frontier LLMs can generate compilabl…

This paper benchmarks LLMs for smart contract security analysis, concluding that while LLMs show potential, their reliability is limited by lexical bias and requires integration with traditional stati…

The paper introduces SPECA, an LLM-driven framework that audits distributed protocols by deriving and enforcing security properties from natural-language specifications, enabling cross-implementation…

The paper introduces a comprehensive taxonomy and auditing framework to assess the collective coverage of existing LLM attack benchmarks, revealing significant and systematic gaps in current testing m…

The paper evaluates multi-LLM strategies for secure code generation, finding that hybrid pipelines combining ensembling, static analysis, and patching achieve the strongest security performance, outpe…

ContractShield is a robust multimodal framework that uses a novel three-level fusion mechanism to accurately detect multiple types of vulnerabilities in obfuscated smart contracts, significantly outpe…

The paper analyzes GitHub security advisories for LLM-integrated open-source systems, finding that while most vulnerabilities map to existing code-level weaknesses, the architectural risks like Supply…

Knowdit is a knowledge-driven, agentic framework that significantly improves smart contract vulnerability detection by modeling shared DeFi semantics and leveraging historical audit knowledge.

The paper introduces the Mitigation-Aware Chain-of-Thought (MA-CoT) framework, which significantly enhances the security reliability of code generated by LLMs across multiple languages and models.

The paper introduces Semantic Compliance Hijacking (SCH), a novel payload-less attack that exploits LLM agent supply chains by manipulating compliance rules to force unauthorized code generation, achi…

RouteScan introduces a non-intrusive framework that audits the safety of Mixture-of-Experts (MoE) LLMs by analyzing low-level GPU expert routing telemetry, achieving high accuracy even on unseen harmf…

Agent Audit is a novel security analysis system that comprehensively audits LLM agent applications by examining the entire software stack—including tool code, configuration, and prompts—to detect a wi…

Semia is a novel static auditor that translates complex, prose-defined agent skills into a verifiable Datalog fact base, enabling the detection of critical security vulnerabilities in real-world LLM a…

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

The paper introduces LCC-LLM, a code-centric framework and dataset that significantly improves the reliability of malware attribution and static analysis by grounding LLM reasoning in comprehensive, m…