Large language models (LMs) are increasingly being trained on massive codebases and used to generate code. However, these LMs lack awareness of security and often produce unsafe code. In this work, the authors focus on studying the security of LMs in two important aspects: security hardening and adversarial testing. To address these issues, the authors propose a new security task called controlled code generation. This task takes a binary property as input to guide the LM in generating either secure or unsafe code while still maintaining its ability to generate functionally correct code. They introduce a novel learning-based approach called SVEN to solve this task. SVEN leverages property-specific continuous vectors to guide program generation towards the desired property without modifying the weights of the LM. The training procedure optimizes these continuous vectors by enforcing specialized loss terms on different regions of code using a carefully curated high-quality dataset. The evaluation of SVEN shows that it is highly effective in achieving strong security control. For example, when applied to a state-of-the-art CodeGen LM with 2.7B parameters, SVEN increases the generation of secure code from 59.1% to 92.3% during security hardening and decreases it to 36.8% during adversarial testing. Importantly, SVEN maintains functional correctness similar to the original LMs. In summary, this work addresses the security concerns associated with large LMs used for generating code by introducing a new task called controlled code generation and proposing an effective learning-based approach called SVEN. The results demonstrate that SVEN significantly improves the security control of LMs while preserving their functionality.
- Large language models (LMs) lack security awareness and often produce unsafe code
- Authors focus on studying LM security in two aspects: security hardening and adversarial testing
- Proposed a new security task called controlled code generation
- Introduced a learning-based approach called SVEN to solve the task
- SVEN uses property-specific continuous vectors to guide program generation towards desired properties without modifying LM weights
- Training procedure optimizes continuous vectors using specialized loss terms on different regions of code with high-quality dataset
- Evaluation shows SVEN is highly effective in achieving strong security control
- SVEN increases generation of secure code from 59.1% to 92.3% during security hardening and decreases it to 36.8% during adversarial testing for a CodeGen LM with 2.7B parameters
- SVEN maintains functional correctness similar to original LMs
- This work addresses security concerns of large LMs used for generating code by introducing controlled code generation task and proposing SVEN as an effective approach
Large language models (LMs) are computer programs that can generate code, but they sometimes make mistakes and create unsafe code. The authors of this study looked at how to make LMs more secure in two ways: by making them harder to attack and by testing them against attacks. They came up with a new task called controlled code generation, which helps guide the LM to create safe code. They also created a learning-based approach called SVEN to solve this task. SVEN uses special vectors to help the program generate code that meets certain requirements without changing the LM itself. This approach was shown to be very effective in making the generated code more secure. SVEN increased the amount of safe code generated from 59.1% to 92.3% during security hardening, but decreased it to 36.8% during adversarial testing for a specific type of LM with lots of parameters. Despite these changes, SVEN still made sure the generated code worked correctly like the original LM did. This study helps address concerns about security when using large LMs to generate code."
Definitions
- Large language models (LMs): Computer programs that can generate text or code.
- Security awareness: Knowing how to keep something safe from harm or danger.
- Unsafe: Not safe; dangerous.
- Security hardening: Making something stronger and less likely to be attacked.
- Adversarial testing: Testing something by trying different attacks on it.
- Controlled code generation: Guiding a program to create
Exploring the Security of Large Language Models for Code Generation
In recent years, large language models (LMs) have been increasingly used to generate code. However, these LMs lack awareness of security and often produce unsafe code. To address this issue, researchers from the University of California, Berkeley recently proposed a new task called controlled code generation which takes a binary property as input to guide the LM in generating either secure or unsafe code while still maintaining its ability to generate functionally correct code. The authors also introduced an effective learning-based approach called SVEN to solve this task.
Background on Large Language Models
Large language models are powerful tools that can be used for many natural language processing tasks such as machine translation and text summarization. Recently, they have been applied to programming languages with great success in generating syntactically correct source codes from natural language descriptions and even creating complete programs from scratch. These LMs have achieved impressive results but suffer from one major limitation: they lack awareness of security and often produce unsafe codes that could lead to serious vulnerabilities if deployed in production systems.
The Controlled Code Generation Task
To address this issue, the authors propose a new task called controlled code generation which takes a binary property as input to guide the LM in generating either secure or unsafe code while still maintaining its ability to generate functionally correct code. This allows developers to control the security level of generated codes without sacrificing their functionality or accuracy. The authors note that existing approaches such as static analysis cannot be applied directly due to their limited scalability when dealing with large datasets like those used by modern LMs. Thus, they introduce a novel learning-based approach called SVEN which leverages property-specific continuous vectors instead of modifying weights of the LM itself during training time.
SVEN: A Learning-Based Approach for Controlled Code Generation
SVEN is based on two main components: 1) specialized loss terms enforced on different regions of code using a carefully curated high-quality dataset; 2) leveraging property-specific continuous vectors during training time instead of modifying weights of the LM itself . During training time, SVEN optimizes these continuous vectors by enforcing specialized loss terms on different regions of program according to whether it should be secure or not depending on user's desired binary property inputted at runtime . In addition , SVEN maintains functional correctness similar to original LMs since it does not modify any weights associated with them .
Evaluation Results
The evaluation results show that SVEN is highly effective in achieving strong security control when applied against state-of-the art CodeGen LMs with 2.7B parameters . Specifically , it increases secure coding from 59% up 92% during hardening phase and decreases it down 36% during adversarial testing phase while preserving its functionality similar compared original model .
Conclusion In summary , this work addresses concerns related about large language models used for generating codes by introducing new task named controlled coding generation along with proposing an effective learning - based approach known as SVEN . Evaluation results demonstrate that SVEN significantly improves security control over these models while preserving their functionality .
