Towards Action Hijacking of Large Language Model-based Agent

• URL: http://arxiv.org/abs/2412.10807v2
• Date: Thu, 12 Jun 2025 10:17:02 GMT
• Title: Towards Action Hijacking of Large Language Model-based Agent
• Authors: Yuyang Zhang, Kangjie Chen, Jiaxin Gao, Ronghao Cui, Run Wang, Lina Wang, Tianwei Zhang,
• Abstract summary: We introduce AI$mathbf2$, a novel attack to manipulate the action plans of LLM-based applications.<n>It first collects action-aware knowledge from the victim application.<n>Based on such knowledge, the attacker can generate misleading input, which can mislead the LLM to generate harmful action plans.
• Score: 23.13653350521422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, applications powered by Large Language Models (LLMs) have made significant strides in tackling complex tasks. By harnessing the advanced reasoning capabilities and extensive knowledge embedded in LLMs, these applications can generate detailed action plans that are subsequently executed by external tools. Furthermore, the integration of retrieval-augmented generation (RAG) enhances performance by incorporating up-to-date, domain-specific knowledge into the planning and execution processes. This approach has seen widespread adoption across various sectors, including healthcare, finance, and software development. Meanwhile, there are also growing concerns regarding the security of LLM-based applications. Researchers have disclosed various attacks, represented by jailbreak and prompt injection, to hijack the output actions of these applications. Existing attacks mainly focus on crafting semantically harmful prompts, and their validity could diminish when security filters are employed. In this paper, we introduce AI$\mathbf{^2}$, a novel attack to manipulate the action plans of LLM-based applications. Different from existing solutions, the innovation of AI$\mathbf{^2}$ lies in leveraging the knowledge from the application's database to facilitate the construction of malicious but semantically-harmless prompts. To this end, it first collects action-aware knowledge from the victim application. Based on such knowledge, the attacker can generate misleading input, which can mislead the LLM to generate harmful action plans, while bypassing possible detection mechanisms easily. Our evaluations on three real-world applications demonstrate the effectiveness of AI$\mathbf{^2}$: it achieves an average attack success rate of 84.30\% with the best of 99.70\%. Besides, it gets an average bypass rate of 92.7\% against common safety filters and 59.45\% against dedicated defense.

Related papers

• TopicAttack: An Indirect Prompt Injection Attack via Topic Transition [71.81906608221038]
  Large language models (LLMs) are vulnerable to indirect prompt injection attacks.<n>We propose TopicAttack, which prompts the LLM to generate a fabricated transition prompt that gradually shifts the topic toward the injected instruction.<n>We find that a higher injected-to-original attention ratio leads to a greater success probability, and our method achieves a much higher ratio than the baseline methods.
  arXiv  Detail & Related papers  (2025-07-18T06:23:31Z)
• AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentVigil, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• Defending against Indirect Prompt Injection by Instruction Detection [81.98614607987793]
  We propose a novel approach that takes external data as input and leverages the behavioral state of LLMs during both forward and backward propagation to detect potential IPI attacks.<n>Our approach achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, while reducing the attack success rate to just 0.12% on the BIPIA benchmark.
  arXiv  Detail & Related papers  (2025-05-08T13:04:45Z)
• A Practical Memory Injection Attack against LLM Agents [49.01756339657071]
  MINJA enables the injection of malicious records into the memory bank by only interacting with the agent via queries and output observations. MINJA enables any user to influence agent memory, highlighting practical risks of LLM agents.
  arXiv  Detail & Related papers  (2025-03-05T17:53:24Z)
• UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning [17.448966928905733]
  Large Language Model (LLM) agents equipped with external tools have become increasingly powerful for handling complex tasks. We present UDora, a unified red teaming framework designed for LLM Agents that dynamically leverages the agent's own reasoning processes to compel it toward malicious behavior.
  arXiv  Detail & Related papers  (2025-02-28T21:30:28Z)
• Unveiling Privacy Risks in LLM Agent Memory [40.26158509307175]
  Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations. We propose a Memory EXTRaction Attack (MEXTRA) to extract private information from memory.
  arXiv  Detail & Related papers  (2025-02-17T19:55:53Z)
• The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents [6.829628038851487]
  Large Language Model (LLM) agents are increasingly being deployed as conversational assistants capable of performing complex real-world tasks through tool integration. In particular, indirect prompt injection attacks pose a critical threat, where malicious instructions embedded within external data sources can manipulate agents to deviate from user intentions. We propose a novel perspective that reframes agent security from preventing harmful actions to ensuring task alignment, requiring every agent action to serve user objectives.
  arXiv  Detail & Related papers  (2024-12-21T16:17:48Z)
• Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation [4.241100280846233]
  AI agents, powered by large language models (LLMs), have transformed human-computer interactions by enabling seamless, natural, and context-aware communication.<n>This paper investigates a critical vulnerability: adversarial attacks targeting the LLM core within AI agents.
  arXiv  Detail & Related papers  (2024-12-05T18:38:30Z)
• Universal and Context-Independent Triggers for Precise Control of LLM Outputs [6.390542864765991]
  Large language models (LLMs) have been widely adopted in applications such as automated content generation and even critical decision-making systems. Recent advancements in gradient-based white-box attack techniques have shown promise in tasks like jailbreaks and system prompt leaks. We propose a novel method to efficiently discover such triggers and assess the effectiveness of the proposed attack.
  arXiv  Detail & Related papers  (2024-11-22T05:17:18Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification [35.16099878559559]
  Large language models (LLMs) have experienced significant development and are being deployed in real-world applications. We introduce a new type of attack that causes malfunctions by misleading the agent into executing repetitive or irrelevant actions. Our experiments reveal that these attacks can induce failure rates exceeding 80% in multiple scenarios.
  arXiv  Detail & Related papers  (2024-07-30T14:35:31Z)
• LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models [21.02295266675853]
  We propose a novel black-box jailbreak attack method, Analyzing-based Jailbreak (ABJ)<n>ABJ comprises two independent attack paths, which exploit the model's multimodal reasoning capabilities to bypass safety mechanisms.<n>Our work reveals a new type of safety risk and highlights the urgent need to mitigate implicit vulnerabilities in the model's reasoning process.
  arXiv  Detail & Related papers  (2024-07-23T06:14:41Z)
• 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)
• ShadowCode: Towards (Automatic) External Prompt Injection Attack against Code LLMs [56.46702494338318]
  This paper introduces a new attack paradigm: (automatic) external prompt injection against code-oriented large language models.<n>We propose ShadowCode, a simple yet effective method that automatically generates induced perturbations based on code simulation.<n>We evaluate our method across 13 distinct malicious objectives, generating 31 threat cases spanning three popular programming languages.
  arXiv  Detail & Related papers  (2024-07-12T10:59:32Z)
• GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning [79.07152553060601]
  Existing methods for enhancing the safety of large language models (LLMs) are not directly transferable to LLM-powered agents. We propose GuardAgent, the first LLM agent as a guardrail to other LLM agents. GuardAgent comprises two steps: 1) creating a task plan by analyzing the provided guard requests, and 2) generating guardrail code based on the task plan and executing the code by calling APIs or using external engines.
  arXiv  Detail & Related papers  (2024-06-13T14:49:26Z)
• BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents [26.057916556444333]
  We show that such methods are vulnerable to our proposed backdoor attacks named BadAgent. Our proposed attack methods are extremely robust even after fine-tuning on trustworthy data.
  arXiv  Detail & Related papers  (2024-06-05T07:14:28Z)
• Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications [10.06789804722156]
  We reveal a new threat to LLM-powered applications, termed retrieval poisoning, where attackers can guide the application to yield malicious responses during the RAG process. Our preliminary experiments indicate that attackers can mislead LLMs with an 88.33% success rate, and achieve a 66.67% success rate in the real-world application.
  arXiv  Detail & Related papers  (2024-04-26T07:11:18Z)
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents [47.219047422240145]
  We take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms.
  arXiv  Detail & Related papers  (2024-02-17T06:48:45Z)
• On Prompt-Driven Safeguarding for Large Language Models [172.13943777203377]
  We find that in the representation space, the input queries are typically moved by safety prompts in a "higher-refusal" direction. Inspired by these findings, we propose a method for safety prompt optimization, namely DRO. Treating a safety prompt as continuous, trainable embeddings, DRO learns to move the queries' representations along or opposite the refusal direction, depending on their harmfulness.
  arXiv  Detail & Related papers  (2024-01-31T17:28:24Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [79.0183835295533]
  We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to assess the risk of such vulnerabilities.<n>Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content.<n>We propose two novel defense mechanisms-boundary awareness and explicit reminder-to address these vulnerabilities in both black-box and white-box settings.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)
• Prompt Injection attack against LLM-integrated Applications [37.86878788874201]
  This study deconstructs the complexities and implications of prompt injection attacks on actual LLM-integrated applications. We formulate HouYi, a novel black-box prompt injection attack technique, which draws inspiration from traditional web injection attacks. We deploy HouYi on 36 actual LLM-integrated applications and discern 31 applications susceptible to prompt injection.
  arXiv  Detail & Related papers  (2023-06-08T18:43:11Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)

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.