Enterprise AI Analysis
Security of Language Models for Code: A Systematic Literature Review
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these models are susceptible to security vulnerabilities, drawing increasing research attention from domains such as software engineering, artificial intelligence, and cybersecurity.
Executive Impact
Our comprehensive systematic review involved analyzing 68 papers, uncovering critical insights into CodeLM security.
0
Papers Reviewed
0
Software Eng. Publications
0
AI Publications
0
Security Publications
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Existing research highlights two main categories of attacks: Backdoor Attacks and Adversarial Attacks. Backdoor attacks, often initiated through data or model poisoning, embed hidden triggers that activate malicious behavior (e.g., generating vulnerable code). Adversarial attacks involve subtle input perturbations to mislead the model into incorrect predictions during inference. Both white-box and black-box adversarial attacks have been explored, with black-box methods receiving more attention.
A significant finding is that CodeLMs are vulnerable to these threats across various code-related tasks, including code understanding and generation. For instance, Schuster et al. [142] demonstrated that backdoors can significantly increase the likelihood of generating vulnerable code, and BadCode [154] achieved high success rates in backdoor attacks on code search. Adversarial attacks like DAMP [197] and ALERT [191] have proven effective in misclassifying code and misleading authorship attribution models.
Various defense methods have been developed to counter attacks on CodeLMs, categorized into Backdoor Defenses and Adversarial Defenses. Backdoor defenses focus on detecting and removing poisoned samples (pre-training), preventing backdoor insertion (in-training), or using model unlearning/input filtering (post-training). Examples include CodeDetector [89] for detecting poisoned samples and DeCE [188] for in-training robustness.
Adversarial defenses primarily involve adversarial training, where adversarial samples are incorporated into the training process to enhance model robustness. Methods like RoPGen [96] strengthen authorship attribution models, and CARROTT [205] improves robustness across multiple tasks. Other approaches include model modification (e.g., SVEN [53]) and additional models (e.g., SPACE [95]) to detect and handle adversarial samples. Despite progress, defense research still lags behind attack research, indicating numerous opportunities for future work, especially in multi-scenario defense techniques and leveraging explainability.
Commonly used CodeLMs include CodeBERT, CodeT5, and GPT-2, often evaluated on datasets like CodeSearchNet, CodeXGLUE, and BigCloneBench. Key metrics include Attack Success Rate (ASR) for attacks and Clean Accuracy (CA) for defenses. CodeBLEU is crucial for code generation tasks. Artifact accessibility is also a focus for reproducibility.
98%
Attack Success Rate for CodePoisoner
Enterprise Process Flow
Manual Search
→
Automated Search
→
Deduplication
→
Study Selection
→
Snowballing Search
→
Collection Results
| Attack Type | Description | Key Target | Impact |
|---|---|---|---|
| Data Poisoning | Injects specific triggers into training data. | Training Data | Model misclassification on trigger input. |
| Model Poisoning | Manipulates training process to embed backdoors. | Training Process/Model Weights | Stealthier, persistent malicious behavior. |
| White-box Adversarial | Attacker has full knowledge of the model (architecture, weights, data). | Model Internals | Precise, targeted mispredictions. |
| Black-box Adversarial | Attacker has no internal model knowledge, relies on queries. | Model Outputs (API) | Less precise, but more realistic for real-world scenarios. |
Case Study: GitHub Copilot Vulnerabilities
GitHub Copilot, powered by Codex, has shown vulnerabilities to backdoor attacks. For instance, Schuster et al. [142] demonstrated that backdoors can significantly increase the likelihood of generating vulnerable code. This highlights the critical need for robust defense mechanisms in AI-powered coding assistants to prevent the unintentional introduction of malicious code into projects. Enterprise clients must implement rigorous code review and security auditing protocols.
Calculate Your Potential AI ROI
Estimate the time and cost savings your enterprise could achieve by strategically implementing secure AI solutions.
Annual Savings
$0
Hours Reclaimed
0
Your Secure AI Implementation Roadmap
A phased approach to integrate secure CodeLMs into your enterprise, ensuring robust and reliable performance.
Phase 01: Initial Assessment & Threat Modeling
Conduct a thorough security audit of existing code infrastructure and identify potential CodeLM vulnerabilities. Define enterprise-specific threat models.
Phase 02: Secure Model Integration & Training
Integrate CodeLMs with robust defense mechanisms. Implement adversarial training and data cleansing protocols to mitigate poisoning attacks.
Phase 03: Continuous Monitoring & Adaptation
Establish real-time monitoring for anomalous CodeLM behavior and continuously update defense strategies to counter emerging threats.
Phase 04: Developer Education & Policy Enforcement
Train development teams on secure AI coding practices. Enforce policies for responsible CodeLM use and output validation.
Ready to Enhance Your Code Security with AI?
Our experts are here to help you navigate the complexities of CodeLM security, from attack mitigation to defense strategy implementation. Book a free consultation today.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat