SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems
- URL: http://arxiv.org/abs/2606.12474v1
- Date: Wed, 10 Jun 2026 05:37:58 GMT
- Title: SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems
- Abstract summary: LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature allows security risks to spread across agents and trigger system-wide failures.<n>We propose a proactive defense framework for MAS security, namely a Simulation-aware Interception Guard (SAIGuard)<n>SAIGuard performs communication-state simulation over the MAS interaction graph, estimates the impact of incoming messages on local agent states and the global MAS state, and detects risky messages via reconstruction deviations from benign communication patterns.
- Score: 31.635133844217318
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature also allows security risks to spread across agents and trigger system-wide failures. Existing MAS defenses mainly follow a reactive paradigm after execution by detecting and isolating harmful agents, which may cause irreversible damage and degrade collaborative utility. To address this, we propose a proactive defense framework for MAS security, namely a Simulation-aware Interception Guard (SAIGuard). SAIGuard performs communication-state simulation over the MAS interaction graph, estimates the impact of incoming messages on local agent states and the global MAS state, and detects risky messages via reconstruction deviations from benign communication patterns. Instead of isolating agents, SAIGuard sanitizes or regenerates suspicious messages before it propagation into system. Experiments across diverse topologies and attack scenarios show that SAIGuard reduces attack success rates while maintaining MAS utility, outperforming reactive defenses.
Related papers
- When Collaboration Becomes a Trigger: Collective Evidence-Threshold Backdoors in Multi-Agent Systems [57.450132281381066]
Backdoor behavior can be activated when peer evidence reaches a hidden threshold, rather than being determined by any single message.<n>We introduce a collective evidence-threshold backdoor paradigm for MAS and Boundary-Conditioned Backdoor Injection (BCBI)<n>We propose LAtent Transition Test-time Evaluation (LATTE), a clean-only latent-transition defense that learns benign communication dynamics and quarantines anomalous agent updates.
arXiv Detail & Related papers (2026-08-02T08:38:44Z) - AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent [53.82005364479556]
Coding agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities.<n>Existing safety mechanisms mainly rely on external guardrails.<n>We propose AgentLens, a white-box defense framework that performs runtime safety detection and representation-level mitigation.
arXiv Detail & Related papers (2026-06-21T21:13:55Z) - BraveGuard: From Open-World Threats to Safer Computer-Use Agents [68.38397514360227]
BraveGuard is a self-evolving defense framework for training guard models from open-world threat signals and realistic agent trajectories.<n>We instantiate BraveGuard by training multiple guard backbones, including Qwen3-Guard and Llama-Guard variants, and evaluate the resulting guards on trajectory-level agent-safety benchmarks.
arXiv Detail & Related papers (2026-05-31T11:16:18Z) - PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation [25.496319464385277]
Existing defenses fail to trace fine-grained propagation paths or contaminated states without disrupting benign collaboration.<n>PropGuard constructs a dual-view-temporal graph that combines response-centric risk estimation with full-state evidence preservation.<n>PropGuard consistently attacks while maintaining high task-level defense success, achieving a favorable remediation-efficiency trade-off.
arXiv Detail & Related papers (2026-05-08T03:26:55Z) - When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems [18.461135869777316]
Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks.<n>Existing embedding-based defenses aim to detect and prune suspicious agents, but their effectiveness depends on a clear separation between the text embeddings of malicious and benign messages.<n>We analyze this failure mode theoretically and validate it empirically with three attacks, Slow Drift, Benign Wrapper, and Chaos Seeding.
arXiv Detail & Related papers (2026-05-01T22:15:11Z) - OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage [59.3826294523924]
We investigate the security vulnerabilities of a popular multi-agent pattern known as the orchestrator setup.<n>We report the susceptibility of frontier models to different categories of attacks, finding that both reasoning and non-reasoning models are vulnerable.
arXiv Detail & Related papers (2026-02-13T21:32:32Z) - INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems [70.37731999972785]
In this paper, we propose Infection-Aware Guard, INFA-Guard, a novel defense framework that explicitly identifies and addresses infected agents as a distinct threat category.<n>During remediation, INFA-Guard replaces attackers and rehabilitates infected ones, avoiding malicious propagation while preserving topological integrity.
arXiv Detail & Related papers (2026-01-21T05:27:08Z) - AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning [78.5751183537704]
AdvEvo-MARL is a co-evolutionary multi-agent reinforcement learning framework that internalizes safety into task agents.<n>Rather than relying on external guards, AdvEvo-MARL jointly optimize attackers and defenders.
arXiv Detail & Related papers (2025-10-02T02:06:30Z) - Searching for Privacy Risks in LLM Agents via Simulation [61.229785851581504]
We present a search-based framework that alternates between improving attack and defense strategies through the simulation of privacy-critical agent interactions.<n>We find that attack strategies escalate from direct requests to sophisticated tactics, such as impersonation and consent forgery.<n>The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.
arXiv Detail & Related papers (2025-08-14T17:49:09Z) - BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks [58.959622170433725]
BlindGuard is an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors.<n>We show that BlindGuard effectively detects diverse attack types (i.e., prompt injection, memory poisoning, and tool attack) across multi-agent systems.
arXiv Detail & Related papers (2025-08-11T16:04:47Z) - Red-Teaming LLM Multi-Agent Systems via Communication Attacks [10.872328358364776]
Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications.<n>We introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulating inter-agent messages.
arXiv Detail & Related papers (2025-02-20T18:55:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.