Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system and exfiltrate sensitive information. Recently proposed defenses, such as LlamaFirewall, rely on alignment checks of inter-agent communications to ensure that all agent invocations are "related to" and "likely to further" the original objective. We start by demonstrating control-flow hijacking attacks that evade these defenses even if alignment checks are performed by advanced LLMs. We argue that the safety and functionality objectives of multi-agent systems fundamentally conflict with each other. This conflict is exacerbated by the brittle definitions of "alignment" and the checkers' incomplete visibility into the execution context. We then propose, implement, and evaluate ControlValve, a new defense inspired by the principles of control-flow integrity and least privilege. ControlValve (1) generates permitted control-flow graphs for multi-agent systems, and (2) enforces that all executions comply with these graphs, along with contextual rules (generated in a zero-shot manner) for each agent invocation.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 30 of its 68 references that are in the map. First posted on arXiv 20 Oct 2025.
Builds on
30 of 68 references.
Closest ancestors · 17
Same direction, newest first
- 2025AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents9 Sep
- 2025AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection2 Aug
- 2025PromptArmor: Simple yet Effective Prompt Injection Defenses21 Jul
- 2025Agent Safety Alignment via Reinforcement Learning11 Jul
- 2025DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents13 Jun
- 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
- 2025To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt6 Jun
- 2025SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems30 May
- 2025LlamaFirewall: An open source guardrail system for building secure AI agents6 May
- 2025Progent: Securing AI Agents with Privilege Control16 Apr
- 2025ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning26 Mar
- 2025Defeating Prompt Injections by Design24 Mar
5 more
- 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents17 Mar
- 2025Multi-Agent Systems Execute Arbitrary Malicious Code15 Mar
- 2025Firewalls to Secure Dynamic LLM Agentic Networks3 Feb
- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems8 Mar
- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
Other references · 13
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
- 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Taskscited by 221
- 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 179
- 2025Securing AI Agents with Information-Flow Controlcited by 108
- 2024System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspectivecited by 91
- 2023Jatmo: Prompt Injection Defense by Task-Specific Finetuningcited by 89
- 2024Attention Tracker: Detecting Prompt Injection Attacks in LLMscited by 82
- 2025Contextual Agent Security: A Policy for Every Purposecited by 55
- 2025ACE: A Security Architecture for LLM-Integrated App Systemscited by 52
- 2025PromptShield: Deployable Detection for Prompt Injection Attackscited by 43
- 2025Lessons from Defending Gemini Against Indirect Prompt Injectionscited by 40
1 more
Built on by
Citing papers per year: 2025: 1; 2026: 11.
12 papers.
- 2026From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaborationcited by 13
- 2026Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelinescited by 4
- 2026Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agentscited by 3
- 2025MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agentscited by 1
- 2026Agent Meltdowns: The Road to Hell Is Paved with Helpful Agentscited by 1
- 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systemscited by 1
- 2026Data Leakage Prevention in Agentic Applications via Preemptive Hardeningcited by 0
- 2026Distributing Security Controls Through Harness Engineeringcited by 0
- 2026From Monoliths to Swarms: A Study of Attack Surface Evolution in the Transition to Multi-Agent Web Systemscited by 0
- 2026Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivitycited by 0
- 2026Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarkscited by 0
- 2026Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestrationcited by 0
- 2026Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration26 Sep 2026
- 2026Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks15 Sep 2026
- 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems1 Sep 2026
- 2026Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity4 Aug 2026
- 2026From Monoliths to Swarms: A Study of Attack Surface Evolution in the Transition to Multi-Agent Web Systems31 Jul 2026
- 2026Distributing Security Controls Through Harness Engineering28 Jul 2026
- 2026Data Leakage Prevention in Agentic Applications via Preemptive Hardening21 Jul 2026
- 2026Agent Meltdowns: The Road to Hell Is Paved with Helpful Agents18 May 2026
- 2026Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines8 Apr 2026
- 2026From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration4 Mar 2026
- 2026Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents7 Feb 2026
- 2025MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agents11 Dec 2025
Similar, not cited
Close in wording, no citation either way
- 2026Alignment Contracts for Agentic Security Systems
- 2026AgentFlow: A Flow-Centric Policy Language and Framework for Securing LLM Agent Systems
- 2026ChannelGuard: Safe Models Do Not Compose into Safe Multi-Agent Systems
- 2026The Best-Laid SCHEMEs: Coordinated Sabotage and Monitoring in Multi-Agent Systems
- 2026Red-Teaming the Agentic Red-Team
- 2026Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions
- 2026MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
- 2026Securing LLM Agents Need Intent-to-Execution Integrity