Vulnerable Code Search: Transferable Attack for Code Language Models

Large Language Model Vulnerabilities

Semantically-Equivalent Transformations-Based Backdoor Attacks against Neural Code Models: Characterization and Mitigation

Neural code models are increasingly embedded in software development workflows, but their susceptibility to backdoor attacks presents a significant security risk. Existing studies have mainly examined injection-based attacks, which insert anomalous patterns into code and can often be weakened by standard sanitization and screening. This focus may create a false sense of security regarding backdoor attacks. In this paper, we introduce Semantically-Equivalent Transformation (SET)-based backdoor attacks, a new class of attacks that use semantics-preserving low-prevalence code transformations to generate stealthy triggers. We further propose Stylistic Pattern Backdoor Triggers (SPBT), a framework for constructing and prioritizing such triggers. Our experiments across five tasks and six languages, using models including CodeBERT, CodeT5, and StarCoder, show that SET-based attacks achieve attack success rates comparable to injection-based counterparts while preserving model utility. More importantly, SET-based attacks proved difficult for the evaluated automated defenses and human inspectors to detect, showing substantially lower detectability than injection-based counterparts. We evaluate normalization-based countermeasures and find they offer only partial mitigation. These results motivate further investigation into scalable defenses tailored to SET-based attacks.

Empirical Study of Code Large Language Models for Binary Security Patch Detection

Security patch detection (SPD) is crucial for maintaining software security, as unpatched vulnerabilities can lead to severe security risks. In recent years, learning-based SPD approaches have achieved promising results on source code. However, they are generally not directly applicable to closed-source and proprietary software, which constitute a substantial portion of real-world systems, because such software typically releases patches only in binary form while its source code remains inaccessible. Despite the impressive performance of code large language models (LLMs) on code intelligence and binary analysis tasks, such as decompilation and compiler optimization, their potential for detecting binary security patches remains largely unexplored, revealing a notable gap between their demonstrated low-level code understanding capabilities and this security-critical task. To narrow this gap, we construct a large-scale binary patch dataset comprising 19,448 samples with two representations, assembly code and pseudo-code, and systematically evaluate 19 code LLMs of varying scales for binary SPD under both prompting and fine-tuning settings. Our initial study demonstrates that directly prompting off-the-shelf code LLMs remains ineffective; even advanced prompting strategies cannot compensate for the lack of task-specific knowledge. In contrast, fine-tuning proves highly effective, with pseudo-code representation consistently yielding the best performance. The best fine-tuned model, LLM4Decompile-9B-v2, achieves 0.915 in accuracy, 0.897 in F1 score, and 0.058 in false positive rate on pseudo-code. On average, models fine-tuned on pseudo-code improve accuracy by 27.5% and F1 score by 46.1%, while reducing the false positive rate by 46.2%, relative to their assembly-based counterparts. To understand this advantage, we analyze pseudo-code and assembly code from the perspectives of embedding-space similarity and code naturalness, and find that pseudo-code more closely resembles source code, making it better aligned with the source-code-centric pretraining of code LLMs. Motivated by this finding, we further augment the pseudo-code dataset with source code data, yielding additional gains after fine-tuning, particularly for smaller models. Beyond in-distribution evaluation, we further assess representative fine-tuned code LLMs under deployment-relevant distribution shifts and find that their robustness varies across shift types. Model performance remains stable under decompiler shift and is relatively preserved under architecture shift, suggesting that fine-tuned code LLMs retain meaningful robustness under practical distribution shifts in binary security patch detection.

Understanding and Improving Model Editing for Secure Code Generation

The first systematic study of model editing as a model-level hardening mechanism for secure code generation is conducted, evaluating 3 state-of-the-art editing methods across diverse LLM families and comparing them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, and functional correctness.

MalTotal: Cost-Effective and Language-Agnostic Malicious Code Poisoning Detection for Millions of Repositories

The widespread adoption of open source software (OSS) has introduced significant security risks, with malicious code poisoning attacks increasingly targeting public package registries and open-source platforms. Existing detection approaches, including heuristic-, learning-, and LLM-based methods, suffer from language-specific designs, limited generalization, and high analysis costs, making them unsuitable for large-scale multi-language analysis. To address these challenges, we propose MalTotal, a scalable and cost-effective framework for language-agnostic malicious code detection. MalTotal leverages LLM-assisted semantic reasoning to identify sensitive APIs, perform hybrid semantic slicing, and reconstruct malicious behavior contexts while reducing analysis overhead. Our evaluations show that MalTotal outperforms 8 state-of-the-art baselines, achieving an average F1-score of 93.1% across 5 mainstream languages. Its hybrid slicing reduces LLM token consumption by 94.0%, lowering the analysis cost from \$86.25 to \$5.19 on 2,168 repositories. In a large-scale study of 120K GitHub repositories containing over 7.3 million files, MalTotal discovered 564 previously unknown malicious repositories across multiple languages at a total cost of \$338. These results demonstrate the effectiveness, scalability, and cost-efficiency of MalTotal in mitigating large-scale code poisoning attacks.

Functional and Secure Code Generation with Task Vectors

A novel method is developed, SecVecCoder, leveraging task vectors to produce trustworthy code that is simultaneously functional and secure without the need for post-generation adjustment, and achieves a decoding latency within 0.6% of the base model's, on average.