StruPhantom: Evolutionary Injection Attacks on Black-Box Tabular Agents Powered by Large Language Models

• URL: http://arxiv.org/abs/2504.09841v1
• Date: Mon, 14 Apr 2025 03:22:04 GMT
• Title: StruPhantom: Evolutionary Injection Attacks on Black-Box Tabular Agents Powered by Large Language Models
• Authors: Yang Feng, Xudong Pan,
• Abstract summary: We present a novel attack termed StruPhantom which specifically targets black-box LLM-powered tabular agents.<n>Our attack achieves over 50% higher success rates than baselines in enforcing the application's response to contain phishing links or malicious codes.
• Score: 25.579489111240136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of autonomous agents powered by large language models (LLMs) has revolutionized popular business applications dealing with tabular data, i.e., tabular agents. Although LLMs are observed to be vulnerable against prompt injection attacks from external data sources, tabular agents impose strict data formats and predefined rules on the attacker's payload, which are ineffective unless the agent navigates multiple layers of structural data to incorporate the payload. To address the challenge, we present a novel attack termed StruPhantom which specifically targets black-box LLM-powered tabular agents. Our attack designs an evolutionary optimization procedure which continually refines attack payloads via the proposed constrained Monte Carlo Tree Search augmented by an off-topic evaluator. StruPhantom helps systematically explore and exploit the weaknesses of target applications to achieve goal hijacking. Our evaluation validates the effectiveness of StruPhantom across various LLM-based agents, including those on real-world platforms, and attack scenarios. Our attack achieves over 50% higher success rates than baselines in enforcing the application's response to contain phishing links or malicious codes.

Related papers

• Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools [10.086284534400658]
  Large language model (LLM) agents have demonstrated remarkable capabilities in complex reasoning and decision-making by leveraging external tools.<n>We identify this as a new and stealthy threat surface that allows malicious tools to be preferentially selected by LLM agents.<n>We propose a black-box in-context learning framework that generates highly attractive but syntactically and semantically valid tool metadata.
  arXiv  Detail & Related papers  (2025-08-04T06:38:59Z)
• 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)
• Benchmarking Misuse Mitigation Against Covert Adversaries [80.74502950627736]
  Existing language model safety evaluations focus on overt attacks and low-stakes tasks.<n>We develop Benchmarks for Stateful Defenses (BSD), a data generation pipeline that automates evaluations of covert attacks and corresponding defenses.<n>Our evaluations indicate that decomposition attacks are effective misuse enablers, and highlight stateful defenses as a countermeasure.
  arXiv  Detail & Related papers  (2025-06-06T17:33:33Z)
• 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)
• CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent [32.958798200220286]
  Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience. We propose a novel attack framework called CheatAgent by harnessing the human-like capabilities of LLMs. Our method first identifies the insertion position for maximum impact with minimal input modification.
  arXiv  Detail & Related papers  (2025-04-13T05:31:37Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks.<n>$textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning.<n>$textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• MELON: Indirect Prompt Injection Defense via Masked Re-execution and Tool Comparison [60.30753230776882]
  LLM agents are vulnerable to indirect prompt injection (IPI) attacks.<n>We present MELON, a novel IPI defense.<n>We show that MELON outperforms SOTA defenses in both attack prevention and utility preservation.
  arXiv  Detail & Related papers  (2025-02-07T18:57:49Z)
• Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models [111.58315434849047]
  We introduce a novel ranking attack framework named Attack-in-the-Chain. It tracks interactions between large language models (LLMs) and Neural ranking models (NRMs) based on chain-of-thought. Empirical results on two web search benchmarks show the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-12-25T04:03:09Z)
• SecAlign: Defending Against Prompt Injection with Preference Optimization [52.48001255555192]
  Adrial prompts can be injected into external data sources to override the system's intended instruction and execute a malicious instruction.<n>We propose a new defense called SecAlign based on the technique of preference optimization.<n>Our method reduces the success rates of various prompt injections to around 0%, even against attacks much more sophisticated than ones seen during training.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• 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)
• Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
  We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena. We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search. We also use ARE to rigorously evaluate how the robustness changes as new components are added.
  arXiv  Detail & Related papers  (2024-06-18T17:32:48Z)
• An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection [17.948513691133037]
  We introduce CodeBreaker, a pioneering LLM-assisted backdoor attack framework on code completion models. By integrating malicious payloads directly into the source code with minimal transformation, CodeBreaker challenges current security measures.
  arXiv  Detail & Related papers  (2024-06-10T22:10:05Z)
• Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications [8.51254190797079]
  We introduce the Raccoon benchmark which comprehensively evaluates a model's susceptibility to prompt extraction attacks. Our novel evaluation method assesses models under both defenseless and defended scenarios. Our findings highlight universal susceptibility to prompt theft in the absence of defenses, with OpenAI models demonstrating notable resilience when protected.
  arXiv  Detail & Related papers  (2024-06-10T18:57:22Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models.<n>We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks.<n>We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment [31.24530091590395]
  We study an attack scenario called Trojan Activation Attack (TA2), which injects trojan steering vectors into the activation layers of Large Language Models. Our experiment results show that TA2 is highly effective and adds little or no overhead to attack efficiency.
  arXiv  Detail & Related papers  (2023-11-15T23:07:40Z)
• Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game [86.66627242073724]
  This paper presents a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking.
  arXiv  Detail & Related papers  (2023-11-02T06:13:36Z)

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.