Policy-Backed Selective Regeneration under Tainted Inter-Agent Communication
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Abstract (arXiv)
Inter-agent communication is essential to multi-agent language-model systems, yet a single message may combine task-critical information with instructions not authorized by the original request. Prompt-based defenses leave enforcement to models exposed to adversarial messages, while indiscriminate message removal discards useful information. We introduce Executable Semantic Commitments with Clean-Room Recovery (ESC-CR), a policy-backed framework for secure inter-agent code generation and recovery. It separates message claims from authorization, constructs executable commitments from trusted tasks, evidence, and policy, and enforces them at an external release boundary. Upon a violation, ESC-CR taints the responsible message and rejected artifact, reconstructs a clean context from evidence-backed task information, and regenerates under the same policy. We evaluate ESC-CR across communication-essential and standard code-generation benchmarks, multiple model families and communication topologies, and adaptive attacks spanning direct, obfuscated, and verifier-aware payloads. Results show that polluted-context retry frequently fails to remove unauthorized influence, while complete message removal can discard information required by communication-essential tasks. ESC-CR preserves evidence-backed claims while suppressing unauthorized releases under matched computational budgets, and the same design transfers to end-to-end agent trajectories.
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References · 17
- 2021Program Synthesis with Large Language Modelscited by 3,509
- 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
- 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 325
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agentscited by 196
- 2025Progent: Securing AI Agents with Privilege Controlcited by 121
- 2025LlamaFirewall: An open source guardrail system for building secure AI agentscited by 114
- 2024The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agentscited by 89
- 2025MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agentscited by 67
- 2025WebInject: Prompt Injection Attack to Web Agentscited by 48
- 2025textitAgents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attackscited by 31
- 2026Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threatscited by 25
- 2026Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agentscited by 18
5 more
- 2026Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agentscited by 18
- 2025Benefits and Limitations of Communication in Multi-Agent Reasoningcited by 7
- 2026Contextualized Privacy Defense for LLM Agentscited by 6
- 2026PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraintscited by 5
- 2025Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbationcited by 2
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