Vulnerable Code Search: Transferable Attack for Code Language Models

• URL: http://arxiv.org/abs/2608.26031v1
• Date: Wed, 26 Aug 2026 17:11:36 GMT
• Title: Vulnerable Code Search: Transferable Attack for Code Language Models
• Abstract summary: We introduce a programming language-agnostic, transferable, adversarial attack that exploits this CLM vulnerability.<n>Our approach perturbs identifiers within a code snippet without altering the snippet's functionality to artificially align the code with a target query.
• Score: 9.326008120835938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable code retrieval is crucial for developer productivity and effective code reuse. However, current neural code language models (CLMs) powering search tools are susceptible to adversarial attacks targeting non-functional textual elements. In this paper, we introduce a programming language-agnostic, transferable, adversarial attack that exploits this CLM vulnerability. Our approach perturbs identifiers within a code snippet without altering the snippet's functionality to artificially align the code with a target query. We demonstrate that our attack, even when computed using smaller code embedding models, such as CodeT5+, is highly effective and transferable to larger, closed-source embedding models, like Voyage-code-3, or LLMs like Gemini-3.1-Pro. Our attack can increase the similarity between the query and arbitrary, irrelevant code snippets, consequently degrading key retrieval metrics such as the Mean Reciprocal Rank (MRR) of state-of-the-art models by up to 77%. The experimental results highlight the fragility of current code search methods and underscore the need for more robust, semantic-aware approaches.

Related papers

• Functional and Secure Code Generation with Task Vectors [7.757776817999749]
  Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities.<n>We develop a novel method, SecVecCoder, leveraging task vectors to produce trustworthy code that is simultaneously functional and secure.
  arXiv  Detail & Related papers  (2026-07-08T19:32:22Z)
• Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation [69.8237598448941]
  This study investigates the potential of ensemble learning to enhance the performance of Large Language Models (LLMs) in source code vulnerability detection.<n>We propose Dynamic Gated Stacking (DGS), a Stacking variant tailored for vulnerability detection.
  arXiv  Detail & Related papers  (2025-09-16T03:48:22Z)
• VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones [11.650715913321076]
  VulCoCo is a lightweight and scalable approach to detect vulnerable code clones.<n>We first construct a synthetic benchmark that spans various clone types.<n>Our experiments show that VulCoCoCo outperforms prior state-of-the-art methods in terms of Precision@k and mean average precision (MAP)
  arXiv  Detail & Related papers  (2025-07-22T14:54:57Z)
• ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding [60.37988508851391]
  Language models (LMs) have become a staple of the code-writing toolbox.<n>Research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better disentangling between syntax and semantics, has been noticeably sparse.<n>In this work, we examine grounding on obfuscated code as a means of helping Code-LMs look beyond the surface-form syntax and enhance their pre-training sample efficiency.
  arXiv  Detail & Related papers  (2025-03-27T23:08:53Z)
• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• 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)
• Double Backdoored: Converting Code Large Language Model Backdoors to Traditional Malware via Adversarial Instruction Tuning Attacks [15.531860128240385]
  This work investigates novel techniques for transitioning backdoors from the AI/ML domain to traditional computer malware.<n>We present MalInstructCoder, a framework designed to assess the cybersecurity vulnerabilities of instruction-tuned Code LLMs.<n>We conduct a comprehensive investigation into the exploitability of the code-specific instruction tuning process involving three state-of-the-art Code LLMs.
  arXiv  Detail & Related papers  (2024-04-29T10:14:58Z)
• 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)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• Multi-context Attention Fusion Neural Network for Software Vulnerability Identification [4.05739885420409]
  We propose a deep learning model that learns to detect some of the common categories of security vulnerabilities in source code efficiently. The model builds an accurate understanding of code semantics with a lot less learnable parameters. The proposed AI achieves 98.40% F1-score on specific CWEs from the benchmarked NIST SARD dataset.
  arXiv  Detail & Related papers  (2021-04-19T11:50:36Z)

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.