TPIA: Towards Target-specific Prompt Injection Attack against Code-oriented Large Language Models

• URL: http://arxiv.org/abs/2407.09164v4
• Date: Thu, 16 Jan 2025 07:50:07 GMT
• Title: TPIA: Towards Target-specific Prompt Injection Attack against Code-oriented Large Language Models
• Authors: Yuchen Yang, Hongwei Yao, Bingrun Yang, Yiling He, Yiming Li, Tianwei Zhang, Zhan Qin, Kui Ren, Chun Chen,
• Abstract summary: This paper presents a novel attack paradigm against Code LLMs, namely target-specific prompt injection attack (TPIA) TPIA generates non-functional perturbations containing the information of malicious instructions and inserts them into the victim's code context. We show that our TPIA can successfully attack three representative open-source Code LLMs and two mainstream commercial Code LLM-integrated applications.
• Score: 28.827640446926253
• License:
• Abstract: Recently, code-oriented large language models (Code LLMs) have been widely exploited to simplify and facilitate programming. With these tools, developers can easily generate the desired complete functional code based on incomplete code snippets and natural language prompts. Unfortunately, a few pioneering works revealed that these Code LLMs are vulnerable to backdoor and adversarial attacks. The former poisons the training data or model parameters, hijacking the LLMs to generate malicious code snippets when encountering the trigger. The latter crafts malicious adversarial input codes to reduce the quality of the generated codes. However, both attacks have some inherent limitations: backdoor attacks rely on the adversary's capability of controlling the model training process; adversarial attacks struggle with fulfilling specific malicious purposes. This paper presents a novel attack paradigm against Code LLMs, namely target-specific prompt injection attack (TPIA). TPIA generates non-functional perturbations containing the information of malicious instructions and inserts them into the victim's code context by spreading them into potentially used dependencies (e.g., packages or RAG's knowledge base). It induces the Code LLMs to generate attacker-specified malicious code snippets at the target location. In general, we compress the attacker-specified malicious objective into the perturbation by adversarial optimization based on greedy token search. We collect 13 representative malicious objectives to design 31 threat cases for three popular programming languages. We show that our TPIA can successfully attack three representative open-source Code LLMs (with an ASR of up to 97.9%) and two mainstream commercial Code LLM-integrated applications (with an ASR of over 90%) in all threat cases, using only a 12-token perturbation. Our work alerts a new practical threat of using Code LLMs.

Related papers

• Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks [88.84977282952602]
  A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs) In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents. We conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-12T17:19:36Z)
• Denial-of-Service Poisoning Attacks against Large Language Models [64.77355353440691]
  LLMs are vulnerable to denial-of-service (DoS) attacks, where spelling errors or non-semantic prompts trigger endless outputs without generating an [EOS] token. We propose poisoning-based DoS attacks for LLMs, demonstrating that injecting a single poisoned sample designed for DoS purposes can break the output length limit.
  arXiv  Detail & Related papers  (2024-10-14T17:39:31Z)
• Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context [49.13497493053742]
  This research explores converting a nonsensical suffix attack into a sensible prompt via a situation-driven contextual re-writing. We combine an independent, meaningful adversarial insertion and situations derived from movies to check if this can trick an LLM. Our approach demonstrates that a successful situation-driven attack can be executed on both open-source and proprietary LLMs.
  arXiv  Detail & Related papers  (2024-07-19T19:47:26Z)
• MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants [14.947665219536708]
  We introduce the Malicious Programming Prompt (MaPP) attack, in which an attacker adds a small amount of text to a prompt for a programming task. We show that our prompt strategy can cause an LLM to add vulnerabilities while continuing to write otherwise correct code.
  arXiv  Detail & Related papers  (2024-07-12T22:30:35Z)
• 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)
• Assessing Cybersecurity Vulnerabilities in Code Large Language Models [18.720986922660543]
  EvilInstructCoder is a framework designed to assess the cybersecurity vulnerabilities of instruction-tuned Code LLMs to adversarial attacks. It incorporates practical threat models to reflect real-world adversaries with varying capabilities. We conduct a comprehensive investigation into the exploitability of instruction tuning for coding tasks using three state-of-the-art Code LLM models.
  arXiv  Detail & Related papers  (2024-04-29T10:14:58Z)
• Coercing LLMs to do and reveal (almost) anything [80.8601180293558]
  It has been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. We argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking.
  arXiv  Detail & Related papers  (2024-02-21T18:59:13Z)
• Instruction Backdoor Attacks Against Customized LLMs [37.92008159382539]
  We propose the first instruction backdoor attacks against applications integrated with untrusted customized LLMs. Our attack includes 3 levels of attacks: word-level, syntax-level, and semantic-level, which adopt different types of triggers with progressive stealthiness. We propose two defense strategies and demonstrate their effectiveness in reducing such attacks.
  arXiv  Detail & Related papers  (2024-02-14T13:47:35Z)
• Transfer Attacks and Defenses for Large Language Models on Coding Tasks [30.065641782962974]
  We study the effect of adversarial perturbations on coding tasks with large language models (LLMs) We propose prompt-based defenses that involve modifying the prompt to include examples of adversarially perturbed code and explicit instructions for reversing adversarial perturbations. Our experiments show that adversarial examples obtained with a smaller code model are indeed transferable, weakening the LLMs' performance.
  arXiv  Detail & Related papers  (2023-11-22T15:11:35Z)
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks [99.23352758320945]
  We propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks on large language models (LLMs) Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense first randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs.
  arXiv  Detail & Related papers  (2023-10-05T17:01:53Z)
• Universal and Transferable Adversarial Attacks on Aligned Language Models [118.41733208825278]
  We propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Surprisingly, we find that the adversarial prompts generated by our approach are quite transferable.
  arXiv  Detail & Related papers  (2023-07-27T17:49:12Z)

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.