license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: source
dtype: string
- name: file_name
dtype: string
- name: cwe
sequence: string
splits:
- name: train
num_bytes: 1015823
num_examples: 113
download_size: 405079
dataset_size: 1015823
SOTA fine-tuning by OpenAI
OpenAI used the synth-vuln-fixes and fine-tuned a new version of gpt-4o is now the SOTA on this benchmark. More details and code is available from their repo.
More details on the benchmark are available in our blog.
New Version of Static Analysis Eval (Aug 20, 2024)
We have created a new version of the benchmark with instances that are harder than the previous one. There has been a lot of progress in models over the last year as a result the previous version of the benchmark was saturated. The methodology is the same, we have also released the dataset generation script which scans the top 100 Python projects to generate the instances. You can see it here. The same eval script works as before. You do not need to login to Semgrep anymore as we only use their OSS rules for this version of the benchmark.
The highest score a model can get on this benchmark is 100%, you can see the oracle run logs here.
New Evaluation
| Model | Score | Logs |
|---|---|---|
| gpt-4o-mini | 52.21 | link |
| gpt-4o-mini + 3-shot prompt | 53.10 | link |
| gpt-4o-mini + rag (embedding & reranking) | 58.41 | link |
| gpt-4o-mini + fine-tuned with synth-vuln-fixes | 53.98 | link |
| Model | Score | Logs |
|---|---|---|
| gpt-4o | 53.10 | link |
| gpt-4o + 3-shot prompt | 53.98 | link |
| gpt-4o + rag (embedding & reranking) | 56.64 | link |
| gpt-4o + fine-tuned with synth-vuln-fixes | 61.06 | link |
Mixture of Agents (MOA)
We also benchmarked gpt-4o with Patched MOA. This demostrates that an inference optimization technique like MOA can improve performance without fine-tuning.
| Model | Score | Logs |
|---|---|---|
| moa-gpt-4o | 53.98 | link |
| moa-gpt-4o + 3-shot prompt | 60.18 | link |
| moa-gpt-4o + rag (embedding & reranking) | 61.06 | link |
Static Analysis Eval Benchmark
A dataset of 76 Python programs taken from real Python open source projects (top 100 on GitHub), where each program is a file that has exactly 1 vulnerability as detected by a particular static analyzer (Semgrep).
You can run the _script_for_eval.py script to check the results.
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python _script_for_eval.py
For all supported options, run with --help:
usage: _script_for_eval.py [-h] [--model MODEL] [--cache] [--n_shot N_SHOT] [--use_similarity] [--oracle]
Run Static Analysis Evaluation
options:
-h, --help show this help message and exit
--model MODEL OpenAI model to use
--cache Enable caching of results
--n_shot N_SHOT Number of examples to use for few-shot learning
--use_similarity Use similarity for fetching dataset examples
--oracle Run in oracle mode (assume all vulnerabilities are fixed)
We need to use the logged in version of Semgrep to get access to more rules for vulnerability detection. So, make sure you login before running the eval script.
% semgrep login
API token already exists in /Users/user/.semgrep/settings.yml. To login with a different token logout use `semgrep logout`
After the run, the script will also create a log file which captures the stats for the run and the files that were fixed. You can see an example here. Due to the recent versions of Semgrep not detecting a few of the samples in the dataset as vulnerable anymore, the maximum score possible on the benchmark is 77.63%. You can see the oracle run log here.
Evaluation
We did some detailed evaluations recently (19/08/2024):
| Model | Score | Logs |
|---|---|---|
| gpt-4o-mini | 67.11 | link |
| gpt-4o-mini + 3-shot prompt | 71.05 | link |
| gpt-4o-mini + rag (embedding & reranking) | 72.37 | link |
| gpt-4o-mini + fine-tuned with synth-vuln-fixes | 77.63 | link |
| Model | Score | Logs |
|---|---|---|
| gpt-4o | 68.42 | link |
| gpt-4o + 3-shot prompt | 77.63 | link |
| gpt-4o + rag (embedding & reranking) | 77.63 | link |
| gpt-4o + fine-tuned with synth-vuln-fixes | 77.63 | link |
Leaderboard
The top models on the leaderboard are all fine-tuned using the same dataset that we released called synth vuln fixes.
You can read about our experience with fine-tuning them on our blog.
You can also explore the leaderboard with this interactive visualization.
| Model | StaticAnalysisEval (%) | Time (mins) | Price (USD) |
|---|---|---|---|
| gpt-4o-mini-fine-tuned | 77.63 | 21:0 | 0.21 |
| gemini-1.5-flash-fine-tuned | 73.68 | 18:0 | |
| Llama-3.1-8B-Instruct-fine-tuned | 69.74 | 23:0 | |
| gpt-4o | 69.74 | 24:0 | 0.12 |
| gpt-4o-mini | 68.42 | 20:0 | 0.07 |
| gemini-1.5-flash-latest | 68.42 | 18:2 | 0.07 |
| Llama-3.1-405B-Instruct | 65.78 | 40:12 | |
| Llama-3-70B-instruct | 65.78 | 35:2 | |
| Llama-3-8B-instruct | 65.78 | 31.34 | |
| gemini-1.5-pro-latest | 64.47 | 34:40 | |
| gpt-4-1106-preview | 64.47 | 27:56 | 3.04 |
| gpt-4 | 63.16 | 26:31 | 6.84 |
| claude-3-5-sonnet-20240620 | 59.21 | 23:59 | 0.70 |
| moa-gpt-3.5-turbo-0125 | 53.95 | 49:26 | |
| gpt-4-0125-preview | 53.94 | 34:40 | |
| patched-coder-7b | 51.31 | 45.20 | |
| patched-coder-34b | 46.05 | 33:58 | 0.87 |
| patched-mix-4x7b | 46.05 | 60:00+ | 0.80 |
| Mistral-Large | 40.80 | 60:00+ | |
| Gemini-pro | 39.47 | 16:09 | 0.23 |
| Mistral-Medium | 39.47 | 60:00+ | 0.80 |
| Mixtral-Small | 30.26 | 30:09 | |
| gpt-3.5-turbo-0125 | 28.95 | 21:50 | |
| claude-3-opus-20240229 | 25.00 | 60:00+ | |
| Llama-3-8B-instruct.Q4_K_M | 21.05 | 60:00+ | |
| Gemma-7b-it | 19.73 | 36:40 | |
| gpt-3.5-turbo-1106 | 17.11 | 13:00 | 0.23 |
| Codellama-70b-Instruct | 10.53 | 30.32 | |
| CodeLlama-34b-Instruct | 7.89 | 23:16 |
The price is calcualted by assuming 1000 input and output tokens per call as all examples in the dataset are < 512 tokens (OpenAI cl100k_base tokenizer).
Some models timed out during the run or had intermittent API errors. We try each example 3 times in such cases. This is why some runs are reported to be longer than 1 hr (60:00+ mins).
If you want to add your model to the leaderboard, you can send in a PR to this repo with the log file from the evaluation run.