Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
Vul-RAG: Enhancing LLM-based vulnerability detection via knowledge-level RAG
9 Pith papers cite this work. Polarity classification is still indexing.
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FuzzingBrain V2, a multi-agent LLM system with a novel Suspicious Point abstraction and dual-layer fuzzing, reports 90% detection on a C/C++ benchmark and 29 confirmed zero-day vulnerabilities in real open-source projects.
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
AnyPoC validates LLM bug reports by synthesizing, executing, and independently re-checking proof-of-concept tests, finding 121 new bugs with 108 confirmed across large systems.
QuiLL is a new evaluation pipeline that uses optimized LLM prompts, dynamic in-context learning from an NVD vector store, and a novel accuracy-plus-reasoning metric to benchmark vulnerability detection in real code.
Frontier LLMs detect up to 63% of web vulnerabilities in WordPress plugins with scoped prompts outperforming open-ended ones, but all show low consistency across runs and miss some baseline issues.
RAG frequently degrades LLM malware explanations when structured VirusTotal input is already available by introducing irrelevant context and narrative noise.
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.
citing papers explorer
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Acoda: Adversarial Code Obfuscation for Defending against LLM-based Analysis
Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
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FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction
FuzzingBrain V2, a multi-agent LLM system with a novel Suspicious Point abstraction and dual-layer fuzzing, reports 90% detection on a C/C++ benchmark and 29 confirmed zero-day vulnerabilities in real open-source projects.
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Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
ReasonVul deploys three LLM agents with independent analysis and structured debate to achieve 40% PairAcc and 72.52% F1 on PrimeVul, outperforming baselines by 81% in PairAcc.
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Comprehensive AI governance requires addressing non-model gains
Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
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AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
AnyPoC validates LLM bug reports by synthesizing, executing, and independently re-checking proof-of-concept tests, finding 121 new bugs with 108 confirmed across large systems.
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QuiLL: An LLM-Based Vulnerability Assessment Framework for the Wild
QuiLL is a new evaluation pipeline that uses optimized LLM prompts, dynamic in-context learning from an NVD vector store, and a novel accuracy-plus-reasoning metric to benchmark vulnerability detection in real code.
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Evaluating LLMs for Real-World Web Vulnerability Detection
Frontier LLMs detect up to 63% of web vulnerabilities in WordPress plugins with scoped prompts outperforming open-ended ones, but all show low consistency across runs and miss some baseline issues.
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Evaluating Retrieval-Augmented Generation for Explainable Malware Analysis
RAG frequently degrades LLM malware explanations when structured VirusTotal input is already available by introducing irrelevant context and narrative noise.
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Large Language Model-Based Agents for Software Engineering: A Survey
A literature survey that collects and categorizes 124 papers on LLM-based agents for software engineering from SE and agent perspectives.