Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures (XAMT)

• URL: http://arxiv.org/abs/2512.15790v1
• Date: Mon, 15 Dec 2025 23:04:48 GMT
• Title: Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures (XAMT)
• Authors: Akhil Sharma, Shaikh Yaser Arafat, Jai Kumar Sharma, Ken Huang,
• Abstract summary: Multi-Agent Systems (MAS) are inherently heterogeneous, integrating conventional Multi-Agent Reinforcement Learning (MARL) with emerging Large Language Model (LLM) agent architectures.<n>A critical shared vulnerability is reliance on centralized memory components: the shared Experience Replay (ER) buffer in MARL and the external Knowledge Base (K) in RAG agents.<n>This paper proposes XAMT (Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures), a novel framework that formalizes attack generation as a bilevel optimization problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing operational reliance on complex Multi-Agent Systems (MAS) across safety-critical domains necessitates rigorous adversarial robustness assessment. Modern MAS are inherently heterogeneous, integrating conventional Multi-Agent Reinforcement Learning (MARL) with emerging Large Language Model (LLM) agent architectures utilizing Retrieval-Augmented Generation (RAG). A critical shared vulnerability is reliance on centralized memory components: the shared Experience Replay (ER) buffer in MARL and the external Knowledge Base (K) in RAG agents. This paper proposes XAMT (Bilevel Optimization for Covert Memory Tampering in Heterogeneous Multi-Agent Architectures), a novel framework that formalizes attack generation as a bilevel optimization problem. The Upper Level minimizes perturbation magnitude (delta) to enforce covertness while maximizing system behavior divergence toward an adversary-defined target (Lower Level). We provide rigorous mathematical instantiations for CTDE MARL algorithms and RAG-based LLM agents, demonstrating that bilevel optimization uniquely crafts stealthy, minimal-perturbation poisons evading detection heuristics. Comprehensive experimental protocols utilize SMAC and SafeRAG benchmarks to quantify effectiveness at sub-percent poison rates (less than or equal to 1 percent in MARL, less than or equal to 0.1 percent in RAG). XAMT defines a new unified class of training-time threats essential for developing intrinsically secure MAS, with implications for trust, formal verification, and defensive strategies prioritizing intrinsic safety over perimeter-based detection.

Related papers

• MARTI-MARS$^2$: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation [64.2621682259008]
  Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2)<n>We propose a Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2) to integrate policy learning with multi-agent tree search.<n>We show that MARTI-MARS2 achieves 77.7%, outperforming strong baselines like GPT-5.1 on challenging code generation benchmarks.
  arXiv  Detail & Related papers  (2026-02-08T07:28:44Z)
• NAAMSE: Framework for Evolutionary Security Evaluation of Agents [1.0131895986034316]
  We propose NAAMSE, an evolutionary framework that reframes agent security evaluation as a feedback-driven optimization problem.<n>Our system employs a single autonomous agent that orchestrates a lifecycle of genetic prompt mutation, hierarchical corpus exploration, and asymmetric behavioral scoring.<n>Experiments on Gemini 2.5 Flash demonstrate that evolutionary mutation systematically amplifies vulnerabilities missed by one-shot methods.
  arXiv  Detail & Related papers  (2026-02-07T06:13:02Z)
• 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)
• Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection [76.91230292971115]
  Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks.<n>XG-Guard is an explainable and fine-grained safeguarding framework for detecting malicious agents in MAS.
  arXiv  Detail & Related papers  (2025-12-21T13:46:36Z)
• L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint) [16.291320202524187]
  L2M-AID is a novel framework for Autonomous Industrial Defense using Multi-agent reinforcement learning.<n>It orchestrates a team of collaborative agents, each driven by a Large Language Model (LLM), to achieve adaptive and resilient security.<n>Results demonstrate that L2M-AID significantly outperforms traditional IDS, deep learning anomaly detectors, and single-agent RL baselines.
  arXiv  Detail & Related papers  (2025-10-08T17:46:39Z)
• 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)
• Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning [49.31650627835956]
  Partial agent failure becomes inevitable when systems scale up, making it crucial to identify the subset of agents whose compromise would most severely degrade overall performance.<n>In this paper, we study this Vulnerable Agent Identification (VAI) problem in large-scale multi-agent reinforcement learning (MARL)<n> Experiments show our method effectively identifies more vulnerable agents in large-scale MARL and the rule-based system, fooling system into worse failures, and learning a value function that reveals the vulnerability of each agent.
  arXiv  Detail & Related papers  (2025-09-18T16:03:50Z)
• ALRPHFS: Adversarially Learned Risk Patterns with Hierarchical Fast \& Slow Reasoning for Robust Agent Defense [12.836334933428738]
  Existing defenses rely on "Safety Checks", which struggle to capture the complex semantic risks posed by harmful user inputs or unsafe agent behaviors.<n>We propose a novel defense framework, ALRPHFS (Adversarially Learned Risk Patterns with Hierarchical Fast & Slow Reasoning)<n>ALRPHFS consists of two core components: (1) an offline adversarial self-learning loop to iteratively refine a generalizable and balanced library of risk patterns, and (2) an online hierarchical fast & slow reasoning engine that balances detection effectiveness with computational efficiency.
  arXiv  Detail & Related papers  (2025-05-25T18:31:48Z)
• MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks [104.50239783909063]
  Multimodal large language models with Retrieval Augmented Generation (RAG) have significantly advanced tasks such as multimodal question answering.<n>This reliance on external knowledge poses a critical yet underexplored safety risk: knowledge poisoning attacks.<n>We propose MM-PoisonRAG, the first framework to systematically design knowledge poisoning in multimodal RAG.
  arXiv  Detail & Related papers  (2025-02-25T04:23:59Z)
• Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning [37.80275600302316]
  distributionally robust Markov games (RMGs) have been proposed to enhance robustness in MARL.<n>Two notorious and open challenges are the formulation of the uncertainty set and whether the corresponding RMGs can overcome the curse of multiagency.<n>In this work, we propose a natural class of RMGs inspired by behavioral economics, where each agent's uncertainty set is shaped by both the environment and the integrated behavior of other agents.
  arXiv  Detail & Related papers  (2024-09-30T08:09:41Z)
• AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases [73.04652687616286]
  We propose AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base. Unlike conventional backdoor attacks, AgentPoison requires no additional model training or fine-tuning. On each agent, AgentPoison achieves an average attack success rate higher than 80% with minimal impact on benign performance.
  arXiv  Detail & Related papers  (2024-07-17T17:59:47Z)

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.