ShadowCode: Towards (Automatic) External Prompt Injection Attack against Code LLMs

• URL: http://arxiv.org/abs/2407.09164v6
• Date: Tue, 22 Jul 2025 08:55:25 GMT
• Title: ShadowCode: Towards (Automatic) External Prompt Injection Attack against Code LLMs
• Authors: Yuchen Yang, Yiming Li, Hongwei Yao, Bingrun Yang, Yiling He, Tianwei Zhang, Dacheng Tao, Zhan Qin,
• Abstract summary: 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.
• Score: 56.46702494338318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements have led to the widespread adoption of code-oriented large language models (Code LLMs) for programming tasks. Despite their success in deployment, their security research is left far behind. This paper introduces a new attack paradigm: (automatic) external prompt injection against Code LLMs, where attackers generate concise, non-functional induced perturbations and inject them within a victim's code context. These induced perturbations can be disseminated through commonly used dependencies (e.g., packages or RAG's knowledge base), manipulating Code LLMs to achieve malicious objectives during the code completion process. Compared to existing attacks, this method is more realistic and threatening: it does not necessitate control over the model's training process, unlike backdoor attacks, and can achieve specific malicious objectives that are challenging for adversarial attacks. Furthermore, we propose ShadowCode, a simple yet effective method that automatically generates induced perturbations based on code simulation to achieve effective and stealthy external prompt injection. ShadowCode designs its perturbation optimization objectives by simulating realistic code contexts and employs a greedy optimization approach with two enhancement modules: forward reasoning enhancement and keyword-based perturbation design. We evaluate our method across 13 distinct malicious objectives, generating 31 threat cases spanning three popular programming languages. Our results demonstrate that ShadowCode successfully attacks three representative open-source Code LLMs (achieving up to a 97.9% attack success rate) and two mainstream commercial Code LLM-integrated applications (with over 90% attack success rate) across all threat cases, using only a 12-token non-functional induced perturbation. The code is available at https://github.com/LianPing-cyber/ShadowCodeEPI.

Related papers

• When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs [30.532439965854767]
  Large Language Models (LLMs) have become integral to automated code analysis, enabling tasks such as vulnerability detection and code comprehension.<n>In this paper, we identify and investigate a new class of prompt-based attacks, termed Copy-Guided Attacks (CGA)<n>We show that CGA reliably induces infinite loops, premature termination, false refusals, and semantic distortions in code analysis tasks.
  arXiv  Detail & Related papers  (2025-07-22T17:21:36Z)
• 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)
• 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)
• Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs [6.936401700600395]
  Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements.<n>This is most likely due to the LLM lacking knowledge about some existing attacks and to the generated code being not evaluated in real usage scenarios.<n>We propose a novel approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline.
  arXiv  Detail & Related papers  (2024-11-27T10:48:37Z)
• Defense Against Prompt Injection Attack by Leveraging Attack Techniques [66.65466992544728]
  Large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks.<n>As LLMs continue to evolve, new vulnerabilities, especially prompt injection attacks arise.<n>Recent attack methods leverage LLMs' instruction-following abilities and their inabilities to distinguish instructions injected in the data content.
  arXiv  Detail & Related papers  (2024-11-01T09:14:21Z)
• 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)
• Generalized Adversarial Code-Suggestions: Exploiting Contexts of LLM-based Code-Completion [4.940253381814369]
  adversarial code-suggestions can be introduced via data poisoning and, thus, unknowingly by the model creators. In this paper, we provide a generalized formulation of such attacks, spawning and extending related work in this domain. The latter gives rise to novel and more flexible targeted attack-strategies, allowing the adversary to choose the most suitable trigger pattern for a specific user-group arbitrarily.
  arXiv  Detail & Related papers  (2024-10-14T14:06:05Z)
• Aligning LLMs to Be Robust Against Prompt Injection [55.07562650579068]
  We show that alignment can be a powerful tool to make LLMs more robust against prompt injection attacks. Our method -- SecAlign -- first builds an alignment dataset by simulating prompt injection attacks. Our experiments show that SecAlign robustifies the LLM substantially with a negligible hurt on model utility.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• 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)
• 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)
• ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings [58.82536530615557]
  We propose an Adversarial Suffix Embedding Translation Framework (ASETF) to transform continuous adversarial suffix embeddings into coherent and understandable text. Our method significantly reduces the computation time of adversarial suffixes and achieves a much better attack success rate to existing techniques.
  arXiv  Detail & Related papers  (2024-02-25T06:46:27Z)
• 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)
• 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.