vulnerability-discovery
Safeguard articles tagged "vulnerability-discovery" — guides, analysis, and best practices for software supply chain and application security.
5 articles
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.
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.
Self-healing security runs on Safeguard.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.