3146 matches found
Reinforcement 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 as a promising approach, particularly for fuzzing, test generation, program...
CyberChainBench: 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-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the benchmark...
Evaluating 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 Claude Opus 4.6, Codex GPT-5.4, Gemini 3.1-pro-preview and open-weight models Qwen...
opencode-apk-forge
APKForge - The Dark Version of OpenCode ███╗ ███╗ ██╗...
Code-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 software underpinning digital society. Many of these vulnerabilities remained maske...
Exploit for Use After Free in Redis
CVE-2026-23479 Scanner Redis Use-After-Free vulnerability CV...
Revisiting Vul-RAG: Reproducibility and Replicability of RAG-Based Vulnerability Detection with Open-Weight Models
Large language models LLMs have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation RAG settings. However, for approaches relying on proprietary models and APIs, reproducibility and replicability remain largely unexplored, raising...
Dissecting the Black Box: Circuit-Level Analysis of LLM Vulnerability Detection
Large language models LLMs can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using mechanistic interpretability; analyzing the internal computations of a neural network to understand its reasoning process.Using Circuit Tracer on...
Exploit for SQL Injection in Drupal
CVE-2026-9082 Passive checker for CVE-2026-9082 / SA-CORE-2...
Exploit for Incorrect Resource Transfer Between Spheres in Linux Linux_Kernel
CVE-2026-31431 / Copy Fail Checker 🔒 Linux kernel vulnerabili...
Exploit for Exposure of Sensitive Information to an Unauthorized Actor in Strapi
CVE-2026-27886 Vulnerability Assessment Tool Safely detect wh...
CodeQL 2.25.5
Discover vulnerabilities across a codebase with CodeQL, an industry-leading semantic code analysis engine. CodeQL lets you query code as though it were data. Write a query to find all variants of a vulnerability, eradicating it forever. Then share your query to help others do the same...
Hunting Vulnerability Variants in AI Infra: Measurement and Reference-Driven Detection
AI infra has become a shared execution layer for model training, deployment, and agent orchestration. Because many projects reimplement similar model-centric workflows, a vulnerability disclosed in one repository can recur as a variant in another repository with a related design. Yet the prevalen...
Three Heads Are Better Than One: A Multi-Perspective Reasoning Framework for Enhanced Vulnerability Detection
Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-intensive manual code audits. Recent advancements have shifted towards leveraging large language models LLMs for...
DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization
Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source...
OpenAI Launches Daybreak for AI-Powered Vulnerability Detection and Patch Validation
OpenAI has launched Daybreak , a new cybersecurity initiative that brings together frontier artificial intelligence AI model capabilities and Codex Security to help organizations identify and patch vulnerabilities before attackers find a way in using the same issues. "Daybreak combines the...
Spring Office Hours Podcast: S5E15 - Upgrading Spring and OSS Security
Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun tackle two challenges every Spring developer faces: keeping applications up to date and staying ahead of security vulnerabilities in open source dependencies. They explore how AI...
MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection
Software vulnerability detection is critical for ensuring software security and reliability. Despite recent advances in deep learning, real-world vulnerability datasets suffer from two severe challenges: frequency imbalance and difficulty imbalance. We reinterpret these challenges from an embeddi...
Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software
Automated vulnerability detection in critical-infrastructure software confronts a fundamental barrier: industrial software is routinely deployed as stripped, symbol-free binaries that deprive conventional Software Composition Analysis of the source-level transparency it requires. Existing binary...