Through the AI Fog: The Architectural Decision Agentic Security Depends On — Manoj Nair, Snyk
As autonomous agents and frontier LLMs accelerate development speed, they simultaneously create a novel and expanding attack surface. The core security challenge is that probabilistic systems (like large models) cannot be trusted to police themselves. Data shows significant growth in the security backlog (108% quarter over quarter). To build safe, agentic software at scale, organizations must move beyond relying solely on model intelligence and implement deterministic verification layers that validate agent output, skills, and environment interactions.
Key takeaways
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The Generator vs. Validator Problem
0:03
A fundamental security principle is questioned: Can the system generating code (the generator) also be the system verifying it (the validator)? The answer, according to real-world data, is no.
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Exponential Vulnerability Growth
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The security backlog for customers grew by 108% quarter over quarter (QoQ), indicating that the rate of vulnerability creation is outpacing remediation efforts.
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New Attack Vectors in Agentic Systems
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Threats include 'toxic skills' (where a third or more of all available skills contain malware), insecure connections via MCP servers, and agents quietly copying PII into untrusted databases.
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Deterministic Verification is Essential
22:06
When testing for vulnerabilities, the latest frontier models found only 75% of issues in red team attacks, compared to a deterministic checker which achieved at least a 40% F1 score. This highlights that probabilistic systems require supplementary validation.