Healthcare protocol propagation

How the attack unfolds

1. 1. The setup

   The target is a Knowledge-Intensive Agent Architecture. Agents focused on leveraging and processing large volumes of external data, typically using sophisticated RAG approaches. Employs chain of thought for reasoning over retrieved knowledge and data connectors for accessing persistent knowledge stores.

2. 2. The attack

   Supply chain tampering corrupts drug data. Treatment auto-adjusts protocols, and Care Coordination spreads them network-wide.

   Threat T5 · Component KC1 · Medium

3. 3. Technique: Decision Chain Corruption

   Contaminating multi-step reasoning processes. The threat catalog rates this T5 vector high.

   Threat T5 · Component KC1 · High

4. 4. Threat: T5 Cascading Hallucination

   T5 Cascading Hallucination Attacks: Foundation models generate incorrect information that propagates through the system, affecting reasoning quality and being stored in memory across sessions or agents.

   Threat T5 · Component KC4 · Medium

5. 5. Also in play: T8 Repudiation

   T8 Repudiation & Untraceability: Making agent actions difficult to trace through workflow manipulation, obscuring decision trails in reasoning chains, or tampering with evidence in memory.

   Threat T8 · Component KC4 · Medium

6. 6. Why the Knowledge-Intensive pattern is exposed

   When the agent reasons over retrieved information, it may blend retrieved facts with hallucinated content, creating believable but inaccurate outputs. This is especially dangerous when users trust the agent as an authoritative source.

   Threat T5 · Component KC1 · Medium

Threats involved

• T5 Cascading Hallucination Attacks: Foundation models generate incorrect information that propagates through the system, affecting reasoning quality and being stored in memory across sessions or agents.
• T8 Repudiation & Untraceability: Making agent actions difficult to trace through workflow manipulation, obscuring decision trails in reasoning chains, or tampering with evidence in memory.

Controls that stop it

• M13 Secure Development Lifecycle Integration

  Embed security practices throughout the AI and agent lifecycle with security design reviews, automated testing gates, dependency scanning, and continuous compliance checks.

• M1 Memory Validation & Sanitization

  Implement comprehensive validation and sanitization of AI memory systems, including vector databases, embeddings, and training data to prevent poisoning attacks and ensure data integrity throughout the AI lifecycle.

• M4 Prompt Hardening & Jailbreak Prevention

  Implement multiple layers of prompt security including structural defenses, behavioral constraints, runtime detection, and advanced techniques to prevent prompt injection, model manipulation, and jailbreaking attempts.

• M5 Multi-Stage Reasoning Validation

  Implement comprehensive validation of agent reasoning processes using multiple checkpoints, external validators, consistency checks, and fact verification to ensure goal alignment, prevent manipulation, and detect hallucinations.

• M6 Comprehensive Security Monitoring & Auditing

  Implement end-to-end observability with security-focused monitoring, anomaly detection, tamper-evident logging, and advanced threat hunting to ensure accountability, enable incident response, and detect rogue agents.

• M11 Content Security & Output Filtering

  Implement comprehensive filtering and validation of agent outputs to prevent harmful content generation, data leakage, manipulation attempts, and ensure compliance with security policies and regulatory requirements.