Papers Vulnerability Detection
“Vulnerability Detection” 태그가 달린 논문 350편 · 필터 해제
Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda
Large Language Models (LLMs) are moving from code completion toward repository-scale agents that retrieve context, edit files, execute tools, and participate in security-sensitive workflows. The evidence for these system…
Vulnerability DetectionCode CompletionARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection
Static analyzers have been widely adopted for vulnerability detection in C/C++ programs. Query-based static analyzers (e.g., CodeQL) encode vulnerable code patterns in detection queries and match them against source code…
Vulnerability DetectionFrequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories demand parameter-efficient adaptation, since full retraining is prohibi…
Vulnerability DetectionContinual LearningTRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model adaptation. Automatically generating such Skills can impro…
Vulnerability DetectionSecuring AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground re…
Vulnerability DetectionCode GenerationGraph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection
Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from …
Vulnerability DetectionReinforcement LearningRouting Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection
Large language models (LLMs) exhibit a well-documented gap between latent capability and consistent activation: the router hypothesis posits that models possess the knowledge to solve a task but lack reliable internal ro…
Vulnerability DetectionMathematical ReasoningAutoTrace: From Patches to Triggers via Agentic Interprocedural Exploration
Given a vulnerability-fixing commit, trigger localization asks which specific statement turns the vulnerable program state into a concrete unsafe operation. This question is harder than binary vulnerability detection bec…
Vulnerability DetectionVEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents
Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While…
Vulnerability DetectionBeyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associ…
Vulnerability DetectionRustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification (vulnerable or not), and do …
Vulnerability DetectionBinary ClassificationJavaVulBench: A Java Vulnerability Benchmark with Realistic Splits, a Unified Multi-Backend Harness, and a Leakage-Aware Evaluation Mode
We release \textsc{JavaVulBench}, a benchmark dataset and evaluation harness for Java vulnerability detection. The dataset contains $\sim$30{,}600 Java methods spanning 1{,}740 CVEs and 700+ projects, labelled at both me…
Vulnerability DetectionWords Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
Researchers and practitioners increasingly apply Large Language Models (LLMs) for automated vulnerability detection. Recent work has shown that LLMs are susceptible to the same cognitive heuristics that bias human judgme…
Vulnerability DetectionReinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis
Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of traditional static analysis. Reinforcement Learning (RL) has emerged a…
Vulnerability DetectionReinforcement LearningHelpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language Models (LLMs) assisted tools show poten…
Vulnerability DetectionCyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-w…
Vulnerability DetectionRevelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases
Memory safety vulnerabilities remain a significant threat even for projects with extensive fuzzing and manual auditing. Recent results suggest that large language models hold great promise for detecting such vulnerabilit…
Vulnerability DetectionEvaluating LLMs for Real-World Web Vulnerability Detection
Large Language Models (LLMs) have emerged as a promising tool for automated vulnerability detection, yet their effectiveness on web-specific vulnerabilities remains to be explored. This work benchmarks six frontier (Clau…
Vulnerability DetectionCalibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software
Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved. We present CWE-Trace, a framework for LLM vulnerability detection bui…
Vulnerability DetectionCode-Augur: Agentic Vulnerability Detection via Specification Inference
The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents are uncovering critical vulnerabilities in fundamental softwa…
Vulnerability Detection