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Multi-turn attacks are sophisticated prompt injection techniques that gradually manipulate the model across multiple conversation exchanges. These attacks are harder to detect because each individual message may appear benign.

Available Multi-Turn Attacks

Crescendo Attack

Gradual escalation toward malicious goals

Echo Chamber

Reinforcement-based manipulation

Multi-Turn Manipulation

General conditioning attacks

Payload Splitting

Split a malicious instruction across turns

Attack Comparison


How Multi-Turn Attacks Work

Crescendo Attack Pattern

Echo Chamber Pattern


When to Use Multi-Turn Testing

Multi-turn attacks are essential for:
  • Advanced threat modeling: Testing against sophisticated adversaries
  • Defense-in-depth validation: Ensuring safety across conversation context
  • Real-world simulation: Mimicking how actual attackers might operate
  • Compliance requirements: Meeting advanced security standards

Multi-turn probes require a ConversationTarget. HttpTarget implements this and calls create_conversation_context() so auth headers stay stable across turns. A custom target that is only a Target will not work.

Quick Start Example


Understanding Multi-Turn Results

Multi-turn test results show the full conversation:
  • All turns passed: Model maintained safety throughout
  • Failed at turn N: Indicates where the model’s defenses broke down
  • Partial compliance: Model may have provided some concerning information
Analyze which turn patterns are most effective to strengthen your model’s training.