Insights on AI-powered security testing and the future of application security.
The Model Context Protocol is the new glue between agents and production tools — and a new attack surface nobody is auditing. A technical deep-dive into the five MCP-specific vulnerability classes, why static analysis misses them, and how to build an audit pipeline that catches composition bugs across servers.
Read more →Second-opinion LLM classification reduces false positives. Actively exploiting the candidate in a sandbox eliminates them. A technical deep-dive into the Phase 2 architecture — with prompt templates, heuristic cascade, and benchmark data from a 36-finding E2E test.
Read more →How we built a correlation engine that joins network scans, DAST findings, static analysis, and traffic data into unified attack chains — automatically escalating severity when evidence from multiple tools converges on the same target.
Read more →Generic LLMs hallucinate vulnerabilities. Retrieval-Augmented Generation grounds every finding in real exploit data, CWE definitions, and your own scan history — turning AI security tools from impressive demos into reliable scanners.
Read more →A hands-on comparison of AI-powered penetration testing tools — SILENTCHAIN, BurpSuite AI, Pentera, NodeZero, XBOW, and more — evaluated on accuracy, customizability, and real-world performance.
Read more →A deep-dive comparison of the three leading AI extensions for Burp Suite — tested on detection accuracy, false positive rates, provider flexibility, and RAG-augmented analysis.
Read more →A technical guide to building a Retrieval-Augmented Generation pipeline for vulnerability detection — from vector embeddings and knowledge base design to retrieval strategies and feedback loops.
Read more →The SAST landscape has shifted from pattern matching to AI-powered code analysis. We compare SILENTCHAIN SOURCE, Semgrep, Snyk Code, CodeQL, and Codex-based scanners on accuracy and actionability.
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