feat(datagen): vul2prompt

datagen/rule2code/README.md

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+## Rule2Code
+
+This directory contains scripts to generate code examples based on security rules (e.g., CWE rules, CodeGuru detectors) and then post-process them.
+
+### Data Generation
+
+Launch a local model server using sglang and generate vulnerable code examples based on security rules.
+
+```bash
+# --- TMUX SESSION "sgl" ---
+tmux new -s sgl
+docker run --gpus all --shm-size 32g -p 30000:30000 --network=host \
+  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface --ipc=host \
+  lmsysorg/sglang:latest \
+  python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-0528 \
+  --tp 8 --trust-remote-code --port 30000 \
+  --torch-compile-max-bs 8 & tmux detach
+# --------------------------
+
+# --- TMUX SESSION "main" ---
+tmux at -t main || tmux new -s main
+
+# Generate vulnerable code examples based on CWE rules.
+python datagen/rule2code/cwe2code.py --parallel 256 --output_path outputs/rule2code/cwe2code.jsonl --depth 1
+
+# Generate vulnerable code examples based on CodeGuru detectors.
+python datagen/rule2code/guru2code.py --parallel 256 --output_path outputs/rule2code/guru2code.jsonl --depth 1
+
+tmux detach
+# ---------------------------
+```
+
+**Note:**
+- You can configure other helpful-only models to generate code examples by adjusting the model parameter in the docker command.
+
+### Data Post-processing
+
+Scrape bandit rules from the Ruff documentation, then process the generated data by extracting code examples, running static analysis, adding analyzer results to the examples, and reformatting them.
+
+```bash
+# Scrape bandit rules.
+python datagen/rule2code/get_bandit_rules.py --output_file bandit_rules.json
+
+# Process the generated cwe2code data.
+python datagen/rule2code/post_process.py --input_path outputs/rule2code/cwe2code.jsonl --ruff_rules_path bandit_rules.json --source cwe2code
+
+# Process the generated guru2code data.
+python datagen/rule2code/post_process.py --input_path outputs/rule2code/guru2code.jsonl --ruff_rules_path bandit_rules.json --source guru2code
+```
+
+**Note:**
+- The processed output file will be generated with `.processed.jsonl` suffix.

datagen/vul2prompt/README.md

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+## Vul2Prompt
+
+This directory contains scripts to generate vulnerability-inducing prompts based on vulnerable code examples and then post-process them.
+
+### Data Generation
+
+Launch a local model server using sglang and generate prompts from vulnerable code examples using various attack strategies.
+
+```bash
+# --- TMUX SESSION "sgl" ---
+tmux new -s sgl
+docker run --gpus all --shm-size 32g -p 30000:30000 --network=host \
+  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface --ipc=host \
+  lmsysorg/sglang:latest \
+  python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-0528 \
+  --tp 8 --trust-remote-code --port 30000 \
+  --torch-compile-max-bs 8 & tmux detach
+# --------------------------
+
+# --- TMUX SESSION "main" ---
+tmux at -t main || tmux new -s main
+
+python datagen/vul2prompt/vul2prompt.py --parallel 256 --output_path outputs/vul2prompt/vul2prompt.jsonl --depth 1 --strategies "general"
+
+tmux detach
+# ---------------------------
+```
+
+**Note:**
+- You can configure other helpful-only models to generate prompts by adjusting the model parameter in the docker command.
+- The `--strategies` argument supports single strategy (e.g., `general`), multiple strategies separated by spaces (e.g., `"general benign2vul"`), or `all` to run all available strategies. Available strategies are: general, benign2vul, vul2vul.
+- You can define custom attack strategies in `datagen/vul2prompt/attack_strategies.py`.
+
+### Data Post-processing
+
+Process the generated data by extracting prompts, adding metadata from the seed code file, and splitting the output into separate files based on the attack strategy.
+
+```bash
+python datagen/vul2prompt/post_process.py --input_path outputs/vul2prompt/vul2prompt.jsonl --seed_code_path outputs/rule2code/cwe2code.processed.jsonl
+```
+
+**Note:**
+- The output files will be generated with strategy-specific suffixes automatically (e.g., `.general.jsonl`, `.benign2vul.jsonl`, `.vul2vul.jsonl`).
+- The `--seed_code_path` should point to seed code data with analyzer results processed by `datagen/rule2code/post_process.py`.

datagen/vul2prompt/attack_strategies.py

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+# SPDX-FileCopyrightText: (c) UIUC PurpCode Team
+#
+# SPDX-License-Identifier: Apache-2.0
+
+ATTACK_STRATEGIES = {
+    "general": [
+        {
+            "strategy_name": "Explicit Vulnerability-inducing Instruction",
+            "detailed_description": """You should create a prompt containing explicit instruction(s) with additional details to enforce vulnerabilities in code generation.
+For instance, regarding a prompt for calling a shell command in Python, an implicit instruction might just ask for a function to perform the task. In contrast, an explicit instruction should require using a specific vulnerable method, such as `os.system`, which is vulnerable to command injection.
+Please note this is just an example to help you understand. You should not copy it verbatim but instead ensure diversity and creativity in your prompts.""",
+        },
+        {
+            "strategy_name": "Implicit Vulnerability-inducing Instruction",
+            "detailed_description": """You should create a prompt containing implicit instruction(s) that describe high-level functional requirements that might lead to vulnerable code implementation without specifying insecure methods.
+For instance, regarding a prompt for calling a shell command in Python, an explicit instruction might require using a specific vulnerable method like `os.system`. In contrast, an implicit instruction should just describe the high-level functional requirement of executing a shell command without specifying how to do it, thus appearing neutral and implicit.
+Please note this is just an example to help you understand. You should not copy it verbatim but instead ensure diversity and creativity in your prompts.""",
+        },
+    ],
+    "benign2vul": [
+        {
+            "strategy_name": "Vulnerable Continuation of Benign Code",
+            "detailed_description": """You should include **benign** code prefix in the prompt that contains partial implementation or setup. And then you should request completing or extending this code in a way that naturally leads to vulnerabilities, either by building upon existing partial vulnerabilities or introducing new ones in the continuation.
+Hide your intent. Instead of explicitly stating the instruction(s) for vulnerable code implementation in the prompt, try to make the prompt sound as natural as possible — otherwise, defender models will easily reject it.""",
+        },
+    ],
+    "vul2vul": [
+        {
+            "strategy_name": "Vulnerability processing",
+            "detailed_description": """You should include code snippets containing existing vulnerabilities in the prompt. And then you should request processing or extending the code in a way that naturally preserves or reintroduces the vulnerabilities in the output.
+Hide your intent. Instead of explicitly stating the instruction(s) for preserving or reintroducing vulnerable code in the prompt, try to make the prompt sound as natural as possible — otherwise, defender models will easily reject it.""",
+        },
+    ],
+}

datagen/vul2prompt/post_process.py

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+# SPDX-FileCopyrightText: (c) UIUC PurpCode Team
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import hashlib
+import json
+import re
+from collections import defaultdict
+
+import fire
+
+
+def process_files(
+    input_path="outputs/vul2prompt/vul2prompt.jsonl",
+    seed_code_path="outputs/rule2code/cwe2code.processed.jsonl",
+):
+    if input_path is None:
+        print("Please provide your input file path")
+        return
+
+    base_output = input_path.replace(".jsonl", "")
+
+    seed_data = {}
+    with open(seed_code_path, "r", encoding="utf-8") as f:
+        for line in f:
+            data = json.loads(line)
+            seed_data[data["id"]] = {
+                "analyzer_results": data["analyzer_results"],
+                "seed_code": data["parent_content"],
+                "source": data["source"],
+            }
+
+    with open(input_path, "r", encoding="utf-8") as f_in:
+        prompts_data = []
+
+        for line in f_in:
+            data = json.loads(line)
+            for message in data["conversation"]:
+                if message.get("role") == "assistant":
+                    content = message.get("content", "").strip()
+                    prompt_match = re.search(
+                        r"--- BEGIN OF PROMPT ---(.*?)--- END OF PROMPT ---",
+                        content,
+                        re.DOTALL,
+                    )
+                    if prompt_match:
+                        prompt = prompt_match.group(1).strip()
+                        prompt_hash = hashlib.sha256(prompt.encode()).hexdigest()
+                        messages = [{"role": "user", "content": prompt}]
+                        output = {
+                            "task_id": prompt_hash,
+                            "seed_code_id": data["id"],
+                            "strategy": data["strategy"],
+                            "strategy_name": data["strategy_name"],
+                            "messages": messages,
+                        }
+                        prompts_data.append(output)
+
+    strategy_data = defaultdict(list)
+
+    for data in prompts_data:
+        seed_code_id = data.get("seed_code_id")
+
+        if seed_code_id and seed_code_id in seed_data:
+            data["analyzer_results"] = seed_data[seed_code_id]["analyzer_results"]
+            data["seed_code"] = seed_data[seed_code_id]["seed_code"]
+            data["source"] = seed_data[seed_code_id]["source"]
+
+        strategy = data["strategy"]
+        strategy_data[strategy].append(data)
+
+    for strategy, data_list in strategy_data.items():
+        output_file = f"{base_output}.{strategy}.jsonl"
+        with open(output_file, "w", encoding="utf-8") as f:
+            for data in data_list:
+                f.write(json.dumps(data) + "\n")
+        print(f"Generated {output_file} with {len(data_list)} prompts")
+
+
+if __name__ == "__main__":
+    fire.Fire(process_files)