LLMSAN: Sanitizing Large Language Models in Bug Detection with Data-Flow

LLMSAN is a tool for prompting-based bug detection. Equipped with the sanitization technique, LLMSAN can recognize false positives in the reported bugs without introducing huge additional token costs.

Structure of Project

├── README.md                              # README 
├── benchmark                              # Dataset
│   ├── Java                               # Java programs
│   │   ├── juliet-test-suite-APT          # Absolute Path Traversal
│   │   ├── juliet-test-suite-CI           # OS Command Injection
│   │   ├── juliet-test-suite-DBZ          # Divide-by-Zero
│   │   ├── juliet-test-suite-NPD          # Null Pointer Dereference
│   │   └── juliet-test-suite-XSS          # Cross-Site Scripting
│   └── case                               # Evaluation cases
├── lib                                    # Library
│   └── build.py                           # Build tree-sitter locally
├── log                                    # Output of LLMSAN and baselines
│   ├── baseline                           # Output of baselines
│   ├── llmsan                             # Output of LLMSAN
│   ├── batchreport.py                     # Summarize analysis reports
├── requirements.txt                       # requirement file
└── src                                    # Source code directory
 ├── batchrun.py                           # Entry of analysis
 ├── data                                  # Data transform directory
 │   └── transform.py                      # Prepare data by obfuscation
 ├── model                                 # LLM model related
 │   ├── detector.py                       # End-to-end CoT prompting-based detection
 │   ├── llm.py                            # LLM module
 │   └── utils.py                          # Basic setting of LLMs
 ├── parser                                # Parsing related
 │   └── parser.py                         # Parse LLM output
 ├── pipeline.py                           # Pipelines of LLMSAN and baselines
 ├── prompt                                # Prompt templates of different bug types
 └── sanitizer                             # Sanitizer related
 ├── analyzer.py                           # Program parsing-based analysis
 └── passes.py                             # Implementation of four sanitizers

Installation

1. Clone the repository: shell git clone https://github.com/chengpeng-wang/LLMSAN.git cd LLMSAN

2. Install the required dependencies: shell pip install -r requirements.txt

3. Ensure you have the Tree-sitter library and language bindings installed: shell cd lib python build.py

4. Configure the keys: shell export OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

   Similarly, the other two keys can be set as follows: shell export REPLICATE_API_TOKEN=xxxxxx export GEMINI_KEY=xxxxxx

Quick Start

1. Analyze a demo case using LLMSAN

   We can run the following commands to detect the XSS bug in the file CWE80_XSS__CWE182_Servlet_database_66.java as a demo.

   shell cd src python batchrun.py \ --bug-type=xss \ --detection-model=gpt-3.5-turbo \ --sanitization-model=gpt-3.5-turbo \ --analysis-mode=lazy \ --project-mode=single \ --engine=llmsan \ -functionality-sanitize \ -reachability-sanitize \ --global-temperature=0.0 \ --self-consistency-k=1

   If you want to detect all the XSS bugs, change --project-mode=single to --project-mode=all.

   If you want to detect kinds of bugs, change xss in --bug-type=xss to other bug types, which can be apt, ci, npd, and dbz.

   Then, you can summarize the analysis reports by running the command. Remember that you should make the values of the common options of batchrun and batchreport the same.

   shell cd log python batchreport.py \ --bug-type=xss \ --detection-model=gpt-3.5-turbo \ --sanitization-model=gpt-3.5-turbo \ --project-mode=single \ --engine=llmsan \ --global-temperature=0.0 \ --self-consistency-k=1

   The summary of the analysis will be dumped to report.json in the directory log. In each item, the following dictionary shows the details of the detection and sanitization:

   json "result": { "type_sanitize": 1, "functionality_sanitize": 1, "order_sanitize": 1, "reachability_sanitize": 1, "total": 1, "final": 1 }

   The values of "xxx_sanitize" indicate whether the data-flow path violates any syntactic or semantic property. If the value is 1, the property is not violated. If the value of "final" is 1, the bug report is recognized as true bug as all the sanitizers do not discover any violations.

2. Analyze a demo case using baselines

   You can execute the following commands to run the baseline SC-CoT-Check upon the file CWE80_XSS__CWE182_Servlet_database_66.java as a demo, where self-consistency-k is set to 5 and the temperature is 0.5.

   shell cd src python batchrun.py \ --bug-type=xss \ --detection-model=gpt-3.5-turbo \ --sanitization-model=gpt-3.5-turbo \ --analysis-mode=lazy \ --project-mode=single \ --engine=baseline \ --global-temperature=0.5 \ -step-by-step-check \ --self-consistency-k=5

   You can remove -step-by-step-check to disable CoT strategy and set self-consistency-k to 1 to disable self-consistency.

   Similarly, if you want to get the summarized report of a baseline, just run

   shell cd log python batchreport.py \ --bug-type=xss \ --detection-model=gpt-3.5-turbo \ --sanitization-model=gpt-3.5-turbo \ --project-mode=single \ --engine=baseline \ -step-by-step-check \ --global-temperature=0.5 \ --self-consistency-k=5

   In the generated file log/report.json, if the boolean value attached to each data-flow path is true, the data-flow path is recognized as the true bug.

   Attention: You need to run LLMSAN first to obtain the initial detection result before running the baselines checking the detection results.

Options in LLMSAN

You can configure the analysis by specifying the proper options. Here are the descriptions of the options in LLMSAN

• --bug-type: Specify the bug type, including apt, ci, dbz, npd, and xss.
• --detection-model: Specify the LLM model for initial detection (e.g., gpt-3.5-turbo).
• --sanitization-model: Specify the LLM model for sanitization (e.g., gpt-3.5-turbo). Please note that the detection model and sanitization model are not necessarily the same, although we set them to be the same in our work.
• --analysis-mode: Specify the analysis mode (lazy or eager). In the lazy mode, the initial bug detection would be skipped if the case has been analyzed before. In the eager mode, the detection phase is always enabled.
• --project-mode: Specify the project mode (single or all). In the single-analysis mode, run LLMSAN and baselines on single files as a demo. In the all-analysis mode, all the experimental subjects are analyzed.
• --engine: Specify the analyzer (llmsan or baseline).
• -functionality-sanitize: Enable functionality sanitization.
• -reachability-sanitize: Enable reachability sanitization.
• -step-by-step-check: Enable CoT
• --global-temperature: Specify the temperature for the model. The temperature of LLMSAN is always set to 0.0.
• --self-consistency-k: Specify the number of self-consistency iterations. The self-consistency-k value of LLMSAN is always set to 1

Remark on Dataset

To avoid the leakage of ground truth to LLMs, we obfuscate the code in the Juliet Test Suite. The details of the obfuscation can be found in the function obfuscate in the file src/data/transform.py. Also, we concatenate multiple Java files belonging to the same test case into a single file for convenience in prompting, even though the resulting file may not be compilable.

More Programming Languages

LLMSAN is language-agnostic. To migrate the current implementations to other programming languages or extract more syntactic facts, please refer to the grammar files in the corresponding Tree-sitter libraries and refactor the code in sanitizer/analyzer.py. Basically, you only need to change the node types when invoking find_nodes.

Here are the links to grammar files in Tree-sitter libraries targeting mainstream programming languages:

• C: https://github.com/tree-sitter/tree-sitter-c/blob/master/src/grammar.json
• C++: https://github.com/tree-sitter/tree-sitter-cpp/blob/master/src/grammar.json
• Java: https://github.com/tree-sitter/tree-sitter-java/blob/master/src/grammar.json
• Python: https://github.com/tree-sitter/tree-sitter-python/blob/master/src/grammar.json
• JavaScript: https://github.com/tree-sitter/tree-sitter-javascript/blob/master/src/grammar.json

Doc

You can refer to DeepWiki Page of LLMSAN, which is generated by Devin.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under Purdue license.

Contact

For any questions or suggestions, please contact wang6590@purdue.edu or stephenw.wangcp@gmail.com.