Solution Brief

Multi-Turn Behavioral Threat Detection

Multi-Turn DetectionFleet IntelligenceRuntime

See Salami Campaigns Across the Full Conversation

Single-message checks can't catch attacks spread innocuously across dozens of turns?

Salami campaigns decompose attacks into individually clean messages — each one passes a per-message classifier, but the pattern is the payload. AgentTrust OS tracks behavioral drift against the agent's own history and fleet baselines across turns, surfacing anomalies that stateless, message-by-message detection is structurally unable to see.

Download this Solution Brief to learn how to:

  • ✓Detect multi-turn injection campaigns that appear benign one message at a time
  • ✓Track behavioral drift against per-agent history and fleet peer baselines
  • ✓Surface salami attacks early enough to route to escalation before the final step fires
Solution Briefs ↗

More from the platform

Explore the other products and deep-dive capability briefs that complete the AgentTrust OS trust layer.

The Three-Layer Trust Platform

Core Products

Capability Deep-Dives

What the platform eliminates

Adversarial Attack Defense

Stop Adversarial Prompts Before They Reach Your Agents

Two-layer semantic defense — confidence gate first, LLM judge second — catches adversarial payloads before any action executes, without relying on pattern lists that attackers already know how to evade.

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Deterministic Enforcement

Make Every Governance Decision Outside the Model

Four injection-proof, model-free deterministic gates evaluate every request before an LLM ever sees the payload — the decision is made and enforced entirely outside the model.

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Framing Attack Prevention

Defeat Framing Attacks That Keyword Filters Miss

Intent-based, pre-execution defense scores confidence first then runs semantic intent evaluation — catches framing attacks without keyword lists that attackers trivially bypass.

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Architecture Hardening

Remove the Model from Your Enforcement Path

Deterministic-first architecture puts four gates in front of every request — the async LLM judge enriches the audit record after the fact, but it never touches the verdict.

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