Zhenyu Chen

Insecure coding preferences in LLM long-term memory can elevate vulnerability rates by over 50%, posing a critical security risk in code generation.

SynapseFlow achieves over 4x higher branch coverage and uncovers critical bugs that other tools miss, revolutionizing fuzz harness generation.

LLMs can now effectively analyze deep learning frameworks for bugs without the need for costly runtime execution, revealing 31 previously undetected issues in PyTorch.

Prompt injection remains the leading attack vector against LLM agents, but emerging threats like persistent state corruption demand urgent attention.

Natural backdoor vulnerabilities are not just a theoretical concern; they are prevalent in CodeLMs and can significantly compromise code security.

Agent-generated feedback not only enhances report quality but also improves task performance and knowledge transfer in crowdsourced testing environments.

LLMs can be taught to avoid repeating past mistakes in vulnerability repair, boosting performance by up to 39% over state-of-the-art methods.

Code dataset watermarking can be stealthy, robust, and effective across both source-code and decompilation tasks by injecting repressible poisoned features that only activate upon watermark removal.

Existing REST API testing tools miss critical business logic, but LoBREST finds 38 bugs that no other tool can.

Most "agent skills" hyped for boosting LLMs in software engineering provide almost no benefit in real-world tasks, with 80% yielding zero pass-rate improvement.