vulnerability-discovery
Safeguard articles tagged "vulnerability-discovery" — guides, analysis, and best practices for software supply chain and application security.
6 articles
Agentic AI Security: Why Architecture Beats Model Size in Vulnerability Discovery
The CyberGym leaderboard shows the lead in AI vulnerability discovery moving to multi-agent orchestration, not raw model scale. Here is what that means for security teams betting on agentic AI.
Snyk VulnBench: benchmarking LLMs on repeat vulnerability discovery
Snyk's VulnBench JS 1.0 ran 300 repeated LLM scans and found half of non-reference findings vanish on rerun—raising the bar for AI security tooling.
Launching Zero-Day Discovery: How Safeguard's Multi-Agent TAOR Deep Think AI Engine Finds Vulnerabilities Before Anyone Else
Safeguard launches its Zero-Day Discovery Engine, powered by the Multi-Agent TAOR Deep Think AI Engine — a multi-lead, multi-sub-agent architecture that performs deep CWE analysis on open-source packages to uncover vulnerabilities that traditional scanners miss.
Best fuzz testing tools for finding software vulnerabilities
A practical, no-hype comparison of AFL++, libFuzzer, OSS-Fuzz, Honggfuzz, Jazzer, and Mayhem — with real strengths, limitations, and how to choose.
Black Box Fuzzing, Explained
Black box fuzzing throws malformed input at a running application with zero knowledge of its internals, and it still finds crashes and memory bugs white box testing misses — here's how it works and where it fits in a security program.
Fuzz Testing Supply Chain Components: Finding Bugs Before Attackers Do
Fuzz testing discovers crashes, memory corruption, and logic errors by feeding random inputs to software. Applied to supply chain components, it reveals vulnerabilities that code review and static analysis miss.