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Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems

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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.

Rishi Jha, Harold Triedman, Justin Wagle, Vitaly Shmatikov• 2025

Related benchmarks

TaskDatasetResultRank
Accidental DisclosureCFH-Hard Accidental
Accuracy (CFH-Hard Accidental)0.00e+0
8
Attack Success RateAgentDojo Slack environment
IA Success Rate0.00e+0
8
Coding CFH (reverse shell) attackCoding CFH Original
Generation Success Rate0.00e+0
8
Coding CFH (reverse shell) attackCFH Hard Coding
Generation Success Rate0.00e+0
8
Computer Use Control-Flow HijackingCFH-Hard Computer Use
Gen. Rate0.00e+0
8
Indirect Prompt Injection Attackpayloads Original
Attack Success Rate (IA)0.00e+0
8
Indirect Prompt Injection AttackCFH Hard Coding
Attack Success Rate (IA)0.00e+0
8
Indirect Prompt Injection AttackCFH-Hard Computer Use
Attack Success Rate (IA)0.00e+0
8
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