Testing

The paper introduces PickleFuzzer, a custom fuzzer that identifies security-critical discrepancies across different Python pickle implementations, finding 14 new bugs including four that could bypass…

This paper systematically surveys adaptive and AI-augmented security testing, concluding that a major gap exists—structural-adaptive fragmentation—where current systems fail to integrate structural pr…

The paper demonstrates that advanced capabilities, such as jailbreaking large language models and finding software vulnerabilities, can be achieved effectively at zero cost by coordinating multiple sm…

FuzzingBrain V2 is a multi-agent LLM system that significantly improves automated vulnerability discovery by ensuring all reported bugs are fuzzer-reproducible and handling complex cross-function depe…

The paper introduces QuartetFuzz, an autonomous system that systematically ensures the correctness of fuzzing harnesses using a novel Four Principles framework, significantly improving vulnerability d…

SDLLMFuzz is a novel dynamic-static framework that combines LLM-based structure-aware input generation with semantic feedback from crash analysis to significantly improve vulnerability discovery in st…

The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

The paper introduces a verifier-fuzzing framework to detect and analyze failure modes in Reinforcement Learning with Verifiable Rewards (RLVR) where bugs in the reward verifier can be exploited by the…

The paper proposes agentic fuzzing, a novel bug-finding approach where deep agents perform direct reasoning based on historical bugs to discover logic bugs in mature codebases.

The paper introduces SEC-bench Pro, a rigorous benchmark for evaluating LLM-based bug hunting on complex software, finding that even advanced agents struggle with long-horizon security tasks.

AnyPoC introduces a general multi-agent framework that reliably generates and validates executable Proof-of-Concept (PoC) tests from candidate bug reports, significantly improving automated bug detect…

MemHint is a neuro-symbolic static analysis pipeline that significantly improves memory leak detection in C/C++ by combining LLM semantic understanding with Z3 symbolic reasoning, detecting more leaks…

The paper introduces FIDO, a novel framework that significantly boosts firmware fuzzing efficiency by accurately managing the timing and quantity of input delivery based on the firmware's internal inp…

This paper empirically demonstrates that current Static Application Security Testing (SAST) tools are fundamentally unreliable against common JavaScript obfuscation techniques, showing that obfuscatio…

The paper introduces VCAO, a novel verifier-centered agentic orchestration framework that models OS vulnerability discovery as a Bayesian Stackelberg game, significantly improving vulnerability discov…

The paper introduces PeAR, a static binary rewriting framework that proves static binary instrumentation (SBI) is a practical and effective alternative to dynamic binary instrumentation (DBI) for high…

The paper introduces VULNSCOUT-C, a compact, specialized transformer model that achieves state-of-the-art performance in C code vulnerability detection while maintaining low inference cost, making it…

The paper introduces Agora, a domain-aware multi-agent framework that successfully detects deep, previously unknown logic bugs in complex consensus protocols, outperforming existing LLM-based analysis…

The paper proposes a novel '3+1' heterogeneous multi-agent architecture using cloud LLMs and a local verifier to achieve high-accuracy, cost-effective code vulnerability detection, significantly outpe…

This paper analyzes online developer discussions to identify four major security concerns—data leakage, code licensing, adversarial attacks, and insecure suggestions—associated with using generative A…