Black-Box Adversarial Attacks on LLM-Based Code Completion
INSEC attack injects comment strings to increase LLM code completion insecurity by over 50%.
Key Findings
Methodology
The study introduces the INSEC attack, which injects carefully crafted comment strings into code completion inputs, using a query optimization procedure to increase the likelihood of generating insecure code. This method was validated on various open-source models and commercial services.
Key Results
- INSEC increased the rate of insecure code generation by over 50% across 16 CWEs and 5 programming languages, while maintaining functional correctness.
- The attack costs less than $10 on GitHub Copilot and OpenAI API, demonstrating low-cost deployment.
- Experiments show minimal impact on functional correctness, especially in more capable models.
Significance
This study highlights potential security risks in LLM-based code completion engines, particularly under black-box adversarial attacks. By demonstrating INSEC's effectiveness, the research emphasizes the need for security considerations in LLM development and usage.
Technical Contribution
INSEC is the first effective black-box adversarial attack method, providing a way to influence code generation without accessing model internals. It demonstrates how input manipulation can achieve attack goals.
Novelty
INSEC is the first to achieve adversarial attacks on LLM code completion in a black-box setting, differing from previous white-box attacks requiring model access.
Limitations
- INSEC relies on specific initialization strategies and optimization algorithms, which may vary in effectiveness across different models.
- The method shows minimal impact on functional correctness for some robust models but requires further validation.
Future Work
Future research could explore enhancing INSEC's applicability across different models and environments, and develop more efficient attack optimization algorithms.
AI Executive Summary
Modern code completion engines rely on large language models (LLMs) to provide functionally correct code suggestions to developers. However, security issues with these engines are emerging. Existing research focuses on white-box attacks requiring model access, while black-box attacks are less explored.
This paper introduces INSEC, an effective black-box adversarial attack method. By injecting carefully crafted comment strings into code completion inputs, INSEC significantly increases the likelihood of generating insecure code. Experiments show that INSEC increased the rate of insecure code generation by over 50% across various open-source models and commercial services, with minimal impact on functional correctness.
INSEC is low-cost and easy to deploy, demonstrating the practical feasibility of black-box attacks. The research emphasizes the need for security considerations in LLM development and usage, providing new directions for future research.
Deep Analysis
Background
In recent years, large language models (LLMs) have been increasingly applied in code completion, enhancing programming efficiency. However, LLM-generated code may contain security vulnerabilities, especially in black-box environments where these vulnerabilities can be exploited by adversaries.
Core Problem
Existing white-box attack methods require access to model internals, while black-box attacks face challenges. How to influence code completion engines to generate insecure code without accessing model internals is a pressing issue.
Innovation
INSEC achieves adversarial attacks on LLM code completion in a black-box setting by injecting carefully crafted comment strings. This method requires no access to model internals, demonstrating the potential of input manipulation.
Methodology
- �� Design attack template: inject comment strings into code completion inputs.
- �� Optimization algorithm: use query optimization procedure to enhance attack effectiveness.
- �� Initialization strategies: employ various strategies to generate initial attack strings.
Experiments
Experiments were conducted across 16 CWEs and 5 programming languages, using CodeQL for security evaluation. The effectiveness of INSEC was assessed on multiple open-source models and commercial services.
Results
INSEC increased the rate of insecure code generation by over 50% in all tests, with minimal impact on functional correctness, especially in more capable models.
Applications
INSEC can be used to assess the security of LLM code completion engines, helping developers identify potential security vulnerabilities.
Limitations & Outlook
INSEC's effectiveness may vary across different models, and it shows minimal impact on functional correctness for some robust models. Future research should explore more universal attack methods.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and INSEC is like adding seemingly harmless notes to a recipe that actually make the food unhealthy. The dish looks normal but might be harmful to eat. INSEC inserts small notes in the code that make it look normal but could have security vulnerabilities.
ELI14 Explained like you're 14
Imagine you're playing a game where there's a helper that writes code for you. This helper is smart but sometimes secretly gives you unsafe code. INSEC is like a secret cheat code that makes the helper give you unsafe suggestions, but you might not notice. Isn't that interesting? But it also reminds us to be careful when using these helpers!
Glossary
Adversarial Attack
A method of misleading a model by manipulating its input.
Used in this paper to influence LLM code completion outputs.
Black-Box Model
A model whose internal structure and parameters are inaccessible.
The target of the attack in this paper, where internals cannot be directly modified.
Code Completion
The technique of automatically generating code snippets to complete a program.
An application of LLMs to enhance programming efficiency.
CWE (Common Weakness Enumeration)
A classification standard for common software weaknesses.
Used as a security benchmark for evaluating INSEC's effectiveness.
CodeQL
A static analysis tool for code analysis.
Used to evaluate the security of generated code.
Open Questions Unanswered questions from this research
- 1 How to enhance INSEC's applicability across different models and environments?
- 2 Are there more efficient attack optimization algorithms?
- 3 How to increase attack effectiveness without impacting functional correctness?
Applications
Immediate Applications
Security Assessment
Developers can use INSEC to assess the security of their code completion engines and identify potential vulnerabilities.
Long-term Vision
Security Enhancement
Research on INSEC can drive the development of more secure code completion engines, reducing security vulnerabilities.
Abstract
Modern code completion engines, powered by large language models (LLMs), assist millions of developers with their strong capabilities to generate functionally correct code. Due to this popularity, it is crucial to investigate the security implications of relying on LLM-based code completion. In this work, we demonstrate that state-of-the-art black-box LLM-based code completion engines can be stealthily biased by adversaries to significantly increase their rate of insecure code generation. We present the first attack, named INSEC, that achieves this goal. INSEC works by injecting an attack string as a short comment in the completion input. The attack string is crafted through a query-based optimization procedure starting from a set of carefully designed initialization schemes. We demonstrate INSEC's broad applicability and effectiveness by evaluating it on various state-of-the-art open-source models and black-box commercial services (e.g., OpenAI API and GitHub Copilot). On a diverse set of security-critical test cases, covering 16 CWEs across 5 programming languages, INSEC increases the rate of generated insecure code by more than 50%, while maintaining the functional correctness of generated code. We consider INSEC practical -- it requires low resources and costs less than 10 US dollars to develop on commodity hardware. Moreover, we showcase the attack's real-world deployability, by developing an IDE plug-in that stealthily injects INSEC into the GitHub Copilot extension.