Continuous Offensive Security: The Line We've Been Walking

2026-05-27 · Source: Blog RSS Feed | Snyk · Field: Technology & Digital — Cybersecurity & Data Privacy, Artificial Intelligence & Machine Learning · Depth: Advanced, long

Summary

Snyk has announced Continuous Offensive Security (COS), an advanced AI pentesting solution designed to address both traditional and AI-specific vulnerabilities. This offering evolves from Dynamic Security Testing, leveraging AI to find context-dependent flaws like BOLA or chained vulnerabilities that previously required human pentesters. COS also introduces Agent Red Teaming to target new attack surfaces created by LLM-integrated applications and AI Agents, automatically triggering when LLM components are detected. Snyk's approach integrates platform context, feeding SAST, SCA, and prior DAST findings to the AI pentester. It employs a hybrid dynamic testing model, using traditional DAST for heuristic-detectable issues and LLMs for complex reasoning, delivering exploit chains rather than isolated alerts. The system is managed by an "AI Security Harness" for enterprise-grade governance and multi-model orchestration.

Key takeaway

For Directors of AI/ML evaluating offensive security solutions, recognize that traditional pentesting models are outpaced by AI-generated code and autonomous attackers. You must adopt continuous offensive security that integrates AI reasoning to find context-dependent and AI-specific vulnerabilities. Prioritize solutions that leverage existing security context and deliver actionable exploit narratives, not just vulnerability lists, to effectively manage your evolving threat landscape and mitigate risks from LLM-integrated applications.

Key insights

AI-driven offensive security is crucial for continuous defense against autonomous attackers and new AI-specific attack surfaces.

Principles

• Context-dependent flaws require reasoning, not just heuristics.
• AI changes pentesting cost, not its core discipline.
• Platform context significantly improves AI pentesting.

Method

Snyk's method involves recon, probing, observing, reasoning, escalating, and validating, enhanced by AI. It includes automated Agent Red Teaming for LLM-integrated components, delivering exploit chains.

In practice

• Integrate existing security findings into AI pentesting.
• Automate red teaming for LLM-integrated applications.
• Focus on exploit chains, not isolated findings.

Topics

• Continuous Offensive Security
• AI Pentesting
• Agent Red Teaming
• API Security
• LLM Security
• Dynamic Application Security Testing

Best for: CTO, VP of Engineering/Data, AI Architect, AI Security Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Blog RSS Feed | Snyk.