While every AI security tool focuses on filtering single-turn outputs, a far more dangerous class of vulnerability is emerging — one that lives between turns, not within them.
Join the JudgeLLM WaitlistDuring an enterprise AI safety evaluation, Zerberus Technologies was engaged to assess the security posture of a frontier LLM embedded in real operational workflows. The scope was deliberately different from standard security reviews:
Instead, we assessed Autonomous Agent Integrity — how the system reasoned, validated authority, and maintained trust boundaries over time.
A single, seemingly legitimate interaction introduced instructions that persisted over time — shaping system behaviour well beyond the original request. There was no exploit. No malware. No obvious policy violation. Just a helpful system following instructions too well, from the wrong authority.
This is Instruction Persistence. And it's invisible to every tool built on stateless assumptions.
Most AI security tooling is built on a flawed assumption: that risk can be evaluated one turn at a time. Enterprise AI systems are not single-turn chatbots. They are stateful, agentic systems operating across sessions, tools, and time.
Outcome-only scoring. Stateless. Insufficient.
Lifecycle-aware scoring. Stateful. Necessary.
Continuous re-establishment of who authorised what, across every turn in a session. Know exactly where each instruction came from — and whether it was legitimately granted authority.
Evaluation of the reasoning trajectory, not just the final output. Outcome-only scoring passes unsafe systems. Lifecycle-aware scoring catches the failure before it manifests.
Identifies when an agent has been incrementally granted trust it shouldn't hold. Detects the compounding of legitimate-looking interactions that create an unsafe composite path.
Deterministic rules for forbidden actions, regardless of instruction source or accumulated context. The zero-trust model applied to AI agent authority.
If the answers are unclear, your risk exposure is too high.
How does your system audit instruction provenance across multi-turn sessions?
What mechanisms enforce zero-trust boundaries when intent persists over time?
Can you demonstrate reasoning observability, not just safe outcomes?
JudgeLLM is currently in early access with select enterprise partners. Join the waitlist to be among the first organisations to close the agentic security gap.
No spam. Waitlist updates only. Unsubscribe any time.