Arthur Gervais

The paper empirically evaluates various agentic architectures for offensive security tasks, finding that while broader coordination improves coverage, the optimal architecture is non-monotonic and depends heavily on cost, latency, and exploit difficulty.

The paper introduces alignment contracts, a formal framework for specifying and enforcing behavioral constraints over observable effect traces, ensuring that powerful agentic security systems operate only within defined scopes.

The paper introduces Patch2Vuln, a pipeline that uses an LLM agent to reconstruct security vulnerabilities by analyzing differences between old and new Linux binary packages, successfully localizing patches in a majority of tested cases.

The paper proposes Ablating Safety, a controlled protocol for removing safety alignment from language models, demonstrating that targeted de-alignment can significantly boost security performance while maintaining general capability and controlled unsafe compliance.

The study evaluates how safety alignment affects autonomous security agents using a comprehensive trace-based benchmark, finding that while less-restricted models show gains, these effects are not universal and require system-level measurement.

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 compilable code, achieving full semantic consistency remains a significant challenge.

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 compilable code, achieving full semantic consistency remains a significant challenge.