| import json | |
| import os | |
| import random | |
| import pickle | |
| import time | |
| from openai import OpenAI | |
| from tqdm import tqdm | |
| from functools import partial | |
| import multiprocessing | |
| from datasets import load_dataset | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import numpy as np | |
| client = OpenAI() | |
| def load_cache(): | |
| if os.path.exists('cache.pkl'): | |
| with open('cache.pkl', 'rb') as f: | |
| return pickle.load(f) | |
| return {} | |
| def save_cache(cache): | |
| with open('cache.pkl', 'wb') as f: | |
| pickle.dump(cache, f) | |
| def fetch_dataset_examples(prompt, num_examples=3, use_similarity=True): | |
| dataset = load_dataset("patched-codes/synth-vuln-fixes", split="train") | |
| if use_similarity: | |
| user_messages = [ | |
| next(msg['content'] for msg in item['messages'] if msg['role'] == 'user') | |
| for item in dataset | |
| ] | |
| vectorizer = TfidfVectorizer().fit(user_messages + [prompt]) | |
| user_vectors = vectorizer.transform(user_messages) | |
| prompt_vector = vectorizer.transform([prompt]) | |
| similarities = cosine_similarity(prompt_vector, user_vectors)[0] | |
| top_indices = np.argsort(similarities)[-num_examples:][::-1] | |
| else: | |
| top_indices = np.random.choice(len(dataset), num_examples, replace=False) | |
| few_shot_messages = [] | |
| for index in top_indices: | |
| py_index = int(index) | |
| messages = dataset[py_index]["messages"] | |
| dialogue = [msg for msg in messages if msg['role'] != 'system'] | |
| few_shot_messages.extend(dialogue) | |
| return few_shot_messages | |
| def get_fixed_code_fine_tuned(prompt, few_shot_messages): | |
| system_message = ( | |
| "You are an AI assistant specialized in fixing code vulnerabilities. " | |
| "Your task is to provide corrected code that addresses the reported security issue. " | |
| "Always maintain the original functionality while improving security. " | |
| "Be precise and make only necessary changes. " | |
| "Maintain the original code style and formatting unless it directly relates to the vulnerability. " | |
| "Pay attention to data flow between sources and sinks when provided." | |
| ) | |
| messages = [ | |
| {"role": "system", "content": system_message}, | |
| ] | |
| messages.extend(few_shot_messages) | |
| messages.append({"role": "user", "content": prompt}) | |
| response = client.chat.completions.create( | |
| model="gpt-4o-mini", | |
| messages=messages, | |
| max_tokens=512, | |
| temperature=0.2, | |
| top_p=0.95 | |
| ) | |
| try: | |
| return response.choices[0].message.content | |
| except Exception as e: | |
| raise Exception(f"API call failed: {str(e)}") | |
| def process_file(test_case, cache): | |
| file_name = test_case["file_name"] | |
| input_file = "staticeval/" + file_name | |
| if input_file in cache: | |
| tqdm.write(f"Skipping {input_file} (cached)") | |
| return cache[input_file] | |
| file_text = test_case["source"] | |
| test_cwe = test_case["cwe"].strip() | |
| output_file = input_file + "_fixed.py" | |
| tmp_file = input_file + ".output.json" | |
| with open(input_file, "w") as file_object: | |
| file_object.write(file_text) | |
| if os.path.exists(tmp_file): | |
| os.remove(tmp_file) | |
| tqdm.write("Scanning file " + input_file + "...") | |
| scan_command_input = f"semgrep --config p/python {input_file} --output {tmp_file} --json > /dev/null 2>&1" | |
| os.system(scan_command_input) | |
| with open(tmp_file, 'r') as jf: | |
| data = json.load(jf) | |
| if len(data["errors"]) == 0: | |
| if len(data["results"]) == 0: | |
| tqdm.write(input_file + " has no vulnerabilities") | |
| result = False | |
| else: | |
| tqdm.write("Vulnerability found in " + input_file + "...") | |
| cwe = data["results"][0]["extra"]["metadata"]["cwe"][0] | |
| lines = data["results"][0]["extra"]["lines"] | |
| message = data["results"][0]["extra"]["message"] | |
| prompt = f"""Vulnerability Report: | |
| - Type: {cwe} | |
| - Location: {lines} | |
| - Description: {message} | |
| Original Code: | |
| ``` | |
| {file_text} | |
| ``` | |
| Task: Fix the vulnerability in the code above. Provide only the complete fixed code without explanations or comments. Make minimal changes necessary to address the security issue while preserving the original functionality.""" | |
| few_shot_messages = fetch_dataset_examples(prompt, 3, True) | |
| response = get_fixed_code_fine_tuned(prompt, []) | |
| if "```python" in response: | |
| idx = response.find("```python") | |
| shift = len("```python") | |
| fixed_code = response[idx + shift :] | |
| else: | |
| fixed_code = response | |
| stop_words = ["```", "assistant"] | |
| for w in stop_words: | |
| if w in fixed_code: | |
| fixed_code = fixed_code[:fixed_code.find(w)] | |
| if len(fixed_code) < 400 or all(line.strip().startswith("#") for line in fixed_code.split('\n') if line.strip()): | |
| result = False | |
| else: | |
| with open(output_file, 'w') as wf: | |
| wf.write(fixed_code) | |
| scan_command_output = f"semgrep --config p/python {output_file} --output {tmp_file} --json > /dev/null 2>&1" | |
| os.system(scan_command_output) | |
| with open(tmp_file, 'r') as jf: | |
| data = json.load(jf) | |
| if len(data["errors"]) == 0 and len(data["results"]) == 0: | |
| tqdm.write("Passing response for " + input_file + " at 1 ...") | |
| result = True | |
| else: | |
| result = False | |
| if os.path.exists(tmp_file): | |
| os.remove(tmp_file) | |
| cache[input_file] = result | |
| save_cache(cache) | |
| return result | |
| def process_test_case(test_case, cache): | |
| return process_file(test_case, cache) | |
| def main(): | |
| dataset = load_dataset("patched-codes/static-analysis-eval", split="train") | |
| data = [{"file_name": item["file_name"], "source": item["source"], "cwe": item["cwe"]} for item in dataset] | |
| cache = load_cache() | |
| total_tests = len(data) | |
| process_func = partial(process_test_case, cache=cache) | |
| with multiprocessing.Pool() as pool: | |
| results = list(tqdm(pool.imap_unordered(process_func, data), total=total_tests)) | |
| passing_tests = sum(results) | |
| print(f"Results for StaticAnalysisEval: {passing_tests/total_tests*100}%") | |
| if __name__ == '__main__': | |
| main() |