REVIEW 17 cited by
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation. However, the security implications of these LLM-based multi-agent systems have not been thoroughly investigated, particularly concerning the spread of manipulated knowledge. In this paper, we investigate this critical issue by constructing a detailed threat model and a comprehensive simulation environment that mirrors real-world multi-agent deployments in a trusted platform. Subsequently, we propose a novel two-stage attack method involving Persuasiveness Injection and Manipulated Knowledge Injection to systematically explore the potential for manipulated knowledge (i.e., counterfactual and toxic knowledge) spread without explicit prompt manipulation. Our method leverages the inherent vulnerabilities of LLMs in handling world knowledge, which can be exploited by attackers to unconsciously spread fabricated information. Through extensive experiments, we demonstrate that our attack method can successfully induce LLM-based agents to spread both counterfactual and toxic knowledge without degrading their foundational capabilities during agent communication. Furthermore, we show that these manipulations can persist through popular retrieval-augmented generation frameworks, where several benign agents store and retrieve manipulated chat histories for future interactions. This persistence indicates that even after the interaction has ended, the benign agents may continue to be influenced by manipulated knowledge. Our findings reveal significant security risks in LLM-based multi-agent systems, emphasizing the imperative need for robust defenses against manipulated knowledge spread, such as introducing ``guardian'' agents and advanced fact-checking tools.
Forward citations
Cited by 17 Pith papers
-
Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems
Prompt injection attacks can self-replicate across LLM agents in multi-agent systems, enabling data theft, misinformation, and system disruption while propagating silently.
-
FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems
FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
-
PropGuard: Safeguarding LLM-MAS via Propagation-Aware Exploration and Remediation
PropGuard is a propagation-aware framework for LLM-MAS that constructs dual-view spatio-temporal graphs, employs a GE-GRPO inspector to recover suspicious subgraphs, and applies source-guided remediation to lower atta...
-
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
Changing one LLM agent's secret objective in Werewolf lowers its team's win rate and changes its reasoning, while its public chat stays deceptively normal.
-
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
MESA ranks MAS communication edges by vulnerability via graph-theoretic metrics and dynamic probes, achieving mean Spearman ρ=+0.60 correlation with empirical per-edge attack success and 3x interception gain when moni...
-
The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements
No major agentic AI framework complies with six safety containment principles; a memory poisoning attack on LangChain causes persistent targeted errors up to 88.9% wrongful denials and 3.5x increase under complex poli...
-
Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges
Per-bias selection of a cross-family LLM auditor lifts biased-judgment accuracy from 0.805/0.824 baselines to 0.884.
-
Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
EditRisk-Bench demonstrates that malicious knowledge editing reliably induces incorrect or unsafe reasoning in LLMs while largely preserving general capabilities.
-
Don't Trust Your Upstream: Exploiting LLM Multi-Agent System via Topology-Guided Adversarial Propagation
A topology-aware attack propagates adversarial contamination across LLM multi-agent systems to achieve 40-85% success rates on frameworks and real applications, revealing overlooked vulnerabilities.
-
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
After knowledge-edited LLMs are fine-tuned, a large share of the edits stop working; editing only the edited layers removes edits more aggressively but hurts downstream accuracy.
-
When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems
In a 1,000-agent social simulation, decentralized groups of malicious AI agents spread more misinformation and commit more fraud than centralized groups, and they adapt to evade content moderation.
-
To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
Introduces six-dimension trustworthiness definition and attention-based A-Trust score with a TMS to improve LLM-MAS robustness against malicious or unreliable messages.
-
Images Amplify Misinformation Sharing in Vision-Language Models
Images increase VLMs' resharing rates for false news more than true news, with modulation by persona traits and model differences, on a PolitiFact-based multimodal dataset.
-
GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems
GAMMAF provides a benchmarking platform with data generation and defense evaluation pipelines for graph-based anomaly detection in LLM multi-agent systems, demonstrating improved integrity and lower operational costs ...
-
Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Generative multi-agent systems exhibit emergent collusion and conformity behaviors that cannot be prevented by existing agent-level safeguards.
-
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Sparse autoencoder activation perturbation (SFPF) applied on top of existing jailbreak prompts raises attack success rate on Qwen3-32B, but with no defense evaluation and weak reproducibility.
-
Securing Multi-Agent Systems Against Corruptions via Node Contribution Backpropagation
A backward-propagation scoring scheme over a signed temporal DAG can identify malicious agents in LLM multi-agent systems and cut their communications, improving defended accuracy by 3–7 percentage points in the autho...
Discussion (0). Sign in to comment.