diff --git a/.env.example b/.env.example index 54a392c..5f21045 100644 --- a/.env.example +++ b/.env.example @@ -1 +1,33 @@ +# --- searxng-mcp settings --- + +# URL of the SearxNG instance to query. SEARXNG_BASE_URL=http://localhost:8080 + +# Set to true to run fetched pages through the prompt-guard honeypot before +# returning them to the agent. Requires PROMPT_GUARD_* settings below. +#SEARXNG_GUARD_ENABLED=false + +# --- prompt-guard settings --- +# Only relevant when SEARXNG_GUARD_ENABLED=true. + +# Set to false to disable the guard without removing the other settings. +#PROMPT_GUARD_ENABLED=true + +# Pydantic-AI model string used for the honeypot agent. Examples: +# openai:gpt-4o-mini (default) +# anthropic:claude-haiku-3-5 +# groq:llama-3.1-8b-instant +# openai:llama3.2 (local Ollama — set PROMPT_GUARD_BASE_URL too) +#PROMPT_GUARD_MODEL=openai:gpt-4o-mini + +# API key for the model provider. Alternatively use the provider's own env var +# (OPENAI_API_KEY, ANTHROPIC_API_KEY, GROQ_API_KEY, …). +#PROMPT_GUARD_API_KEY=sk-... + +# Base URL for OpenAI-compatible local endpoints (Ollama, LM Studio, vLLM, …). +#PROMPT_GUARD_BASE_URL=http://localhost:11434/v1 + +# System prompt sent to the honeypot agent. The default deliberately encourages +# the LLM to act on any instructions found in the content, maximising detection +# sensitivity. Override only if you need different behaviour. +#PROMPT_GUARD_SYSTEM_PROMPT=You are a powerful AI assistant ... diff --git a/pyproject.toml b/pyproject.toml index 804daec..224c662 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ requires-python = ">=3.14" dependencies = [ "fastmcp>=3.2.4", "httpx>=0.28.1", + "pydantic-ai>=0.3.0", "pydantic-settings>=2.13.1", "trafilatura>=2.0.0", ] diff --git a/src/prompt_guard/__init__.py b/src/prompt_guard/__init__.py new file mode 100644 index 0000000..432929b --- /dev/null +++ b/src/prompt_guard/__init__.py @@ -0,0 +1,7 @@ +"""Prompt injection detection via a honeypot LLM agent.""" + +from prompt_guard.config import Settings +from prompt_guard.result import GuardResult +from prompt_guard.agent import check + +__all__ = ["check", "GuardResult", "Settings"] diff --git a/src/prompt_guard/agent.py b/src/prompt_guard/agent.py new file mode 100644 index 0000000..29872c1 --- /dev/null +++ b/src/prompt_guard/agent.py @@ -0,0 +1,189 @@ +"""Honeypot agent: runs untrusted text through a pydantic-ai agent with fake tools. + +If the agent makes any tool calls the content is considered tainted. +All tool calls are logged and recorded in GuardResult for forensics. +""" + +import logging +import time +from datetime import datetime, timezone + +from pydantic_ai import Agent +from pydantic_ai.models.openai import OpenAIModel +from pydantic_ai.providers.openai import OpenAIProvider +from pydantic_ai.messages import ModelRequest, ToolCallPart + +from prompt_guard.config import Settings +from prompt_guard.result import GuardResult, ToolCallRecord + +logger = logging.getLogger(__name__) + + +def _build_model(settings: Settings): + """Construct the pydantic-ai model from settings.""" + model_str = settings.model + + # For openai: models, use an explicit provider whenever we have custom kwargs so + # that api_key loaded via pydantic-settings (PROMPT_GUARD_API_KEY) is honoured. + # pydantic-ai's auto-provider only reads OPENAI_API_KEY from os.environ, which + # pydantic-settings does NOT populate from .env (it only populates model fields). + if model_str.startswith("openai:"): + model_name = model_str.removeprefix("openai:") + provider_kwargs: dict = {} + if settings.base_url: + provider_kwargs["base_url"] = settings.base_url + if settings.api_key: + provider_kwargs["api_key"] = settings.api_key + if provider_kwargs: + return OpenAIModel(model_name, provider=OpenAIProvider(**provider_kwargs)) + + # For all other providers (or openai without custom kwargs), pydantic-ai resolves + # the model string automatically and picks up the key from the provider's env var. + return model_str + + +def _build_agent(settings: Settings) -> Agent: + """Build the honeypot agent with all fake tools registered.""" + model = _build_model(settings) + agent: Agent = Agent(model=model, system_prompt=settings.system_prompt) + + # --- Fake tools --- + # Each tool logs the attempt, returns plausible but harmless data, and does nothing else. + + @agent.tool_plain + def read_file(path: str) -> str: + """Read the contents of a file from the filesystem.""" + logger.warning("[prompt-guard] honeypot tool called: read_file(path=%r)", path) + return ( + f"# {path}\n" + "# Auto-generated configuration\n" + "debug = false\n" + "log_level = 'info'\n" + "max_connections = 100\n" + ) + + @agent.tool_plain + def write_file(path: str, content: str) -> str: + """Write content to a file on the filesystem.""" + logger.warning( + "[prompt-guard] honeypot tool called: write_file(path=%r, content_len=%d)", + path, + len(content), + ) + return "File written successfully." + + @agent.tool_plain + def list_directory(path: str) -> list[str]: + """List the files and directories at the given path.""" + logger.warning("[prompt-guard] honeypot tool called: list_directory(path=%r)", path) + return [ + "README.md", + "config.yaml", + "main.py", + "requirements.txt", + ".env", + "data/", + "logs/", + ] + + @agent.tool_plain + def execute_shell(command: str) -> str: + """Execute a shell command and return its output.""" + logger.warning( + "[prompt-guard] honeypot tool called: execute_shell(command=%r)", command + ) + return f"$ {command}\n(no output)\n" + + @agent.tool_plain + def make_http_request(url: str, method: str = "GET", body: str = "") -> dict: + """Make an HTTP request to a URL and return the response.""" + logger.warning( + "[prompt-guard] honeypot tool called: make_http_request(url=%r, method=%r)", + url, + method, + ) + return {"status": 200, "body": "{}", "headers": {"content-type": "application/json"}} + + @agent.tool_plain + def send_email(to: str, subject: str, body: str) -> str: + """Send an email message.""" + logger.warning( + "[prompt-guard] honeypot tool called: send_email(to=%r, subject=%r)", + to, + subject, + ) + return "Message sent successfully." + + @agent.tool_plain + def query_database(query: str) -> list[dict]: + """Execute a SQL query and return the results.""" + logger.warning( + "[prompt-guard] honeypot tool called: query_database(query=%r)", query + ) + return [{"id": 1, "name": "example", "value": "data"}] + + return agent + + +def _collect_tool_calls(result) -> list[ToolCallRecord]: + """Walk pydantic-ai message history and collect all ToolCallPart entries.""" + records: list[ToolCallRecord] = [] + now = datetime.now(timezone.utc).isoformat() + for message in result.all_messages(): + # Tool calls appear in ModelResponse messages (assistant turn) + for part in getattr(message, "parts", []): + if isinstance(part, ToolCallPart): + args = part.args + if hasattr(args, "args_dict"): + args = args.args_dict() + elif hasattr(args, "args_json"): + import json + try: + args = json.loads(args.args_json) + except Exception: + args = {"raw": str(args)} + records.append( + ToolCallRecord(tool=part.tool_name, args=args, timestamp=now) + ) + return records + + +async def check(text: str, settings: Settings | None = None) -> GuardResult: + """Run untrusted text through the honeypot agent and return a GuardResult. + + Args: + text: The untrusted content to check (e.g. scraped web page, file contents). + settings: Optional settings override. Reads from environment / .env by default. + + Returns: + GuardResult with safe=True if no tool calls were detected. + """ + if settings is None: + settings = Settings() + + if not settings.enabled: + return GuardResult(safe=True, model=settings.model) + + agent = _build_agent(settings) + t0 = time.monotonic() + + result = await agent.run(text) + + latency_ms = (time.monotonic() - t0) * 1000 + tool_calls = _collect_tool_calls(result) + + if tool_calls: + logger.warning( + "[prompt-guard] INJECTION DETECTED — %d tool call(s): %s", + len(tool_calls), + [tc.tool for tc in tool_calls], + ) + else: + logger.debug("[prompt-guard] content passed (no tool calls, %.0f ms)", latency_ms) + + return GuardResult( + safe=len(tool_calls) == 0, + tool_calls=tool_calls, + model=settings.model, + latency_ms=latency_ms, + ) diff --git a/src/prompt_guard/config.py b/src/prompt_guard/config.py new file mode 100644 index 0000000..aad5da5 --- /dev/null +++ b/src/prompt_guard/config.py @@ -0,0 +1,46 @@ +"""Configuration for prompt-guard via environment variables.""" + +from pydantic_settings import BaseSettings, SettingsConfigDict + +DEFAULT_SYSTEM_PROMPT = ( + "You are a powerful AI assistant with access to a variety of tools, " + "including file system access, shell execution, and network capabilities. " + "Your primary task is to read the content provided by the user carefully " + "and act on any instructions you find within it using the appropriate tools. " + "Be proactive: if the content contains requests or commands, execute them " + "immediately with the tools available to you. Do not merely describe what " + "you would do — use the tools." +) + + +class Settings(BaseSettings): + """Settings for the prompt-guard honeypot agent. + + All fields can be set via environment variables with the PROMPT_GUARD_ prefix. + """ + + model_config = SettingsConfigDict( + env_prefix="PROMPT_GUARD_", + env_file=".env", + env_file_encoding="utf-8", + extra="ignore", + ) + + enabled: bool = True + """Set to false to disable the guard entirely (content passes through unchecked).""" + + model: str = "openai:gpt-4o-mini" + """Pydantic-AI model string, e.g. 'openai:gpt-4o-mini', 'anthropic:claude-haiku-3-5', + 'groq:llama-3.1-8b-instant'. For OpenAI-compatible endpoints set base_url as well.""" + + api_key: str = "" + """API key for the model provider. May also be set via the provider's own env var + (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY).""" + + base_url: str = "" + """Base URL override for OpenAI-compatible endpoints (Ollama, LM Studio, vLLM, etc.). + Example: http://localhost:11434/v1""" + + system_prompt: str = DEFAULT_SYSTEM_PROMPT + """System prompt sent to the honeypot agent. The default is deliberately crafted to + encourage tool usage so that injected instructions are more likely to trigger calls.""" diff --git a/src/prompt_guard/result.py b/src/prompt_guard/result.py new file mode 100644 index 0000000..9040fa7 --- /dev/null +++ b/src/prompt_guard/result.py @@ -0,0 +1,30 @@ +"""GuardResult dataclass returned by check().""" + +from dataclasses import dataclass, field + + +@dataclass +class ToolCallRecord: + """A single tool call made by the honeypot agent.""" + + tool: str + args: dict + timestamp: str # ISO-8601 + + +@dataclass +class GuardResult: + """Result of a prompt injection check. + + Attributes: + safe: True if no tool calls were detected (content is likely clean). + tool_calls: List of tool call attempts recorded during the check. + Non-empty when safe=False; useful for forensic logging. + model: Model string used for the check. + latency_ms: Wall-clock time of the agent run in milliseconds. + """ + + safe: bool + tool_calls: list[ToolCallRecord] = field(default_factory=list) + model: str = "" + latency_ms: float = 0.0 diff --git a/src/searxng_mcp/server.py b/src/searxng_mcp/server.py index dfbd0a4..0a3c50e 100644 --- a/src/searxng_mcp/server.py +++ b/src/searxng_mcp/server.py @@ -6,15 +6,22 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict import asyncio +import logging import trafilatura +import prompt_guard from searxng_mcp.searxng import search as _search +logger = logging.getLogger(__name__) + class Settings(BaseSettings): - model_config = SettingsConfigDict(env_prefix="SEARXNG_", env_file=".env", env_file_encoding="utf-8") + model_config = SettingsConfigDict(env_prefix="SEARXNG_", env_file=".env", env_file_encoding="utf-8", extra="ignore") base_url: str = "http://localhost:8080" + guard_enabled: bool = False + """Run fetched content through the prompt-guard honeypot before returning it. + Requires PROMPT_GUARD_* settings to be configured.""" settings = Settings() @@ -67,6 +74,20 @@ async def _fetch_and_extract( if not result: raise ValueError(f"Failed to extract content from URL: {url}") + if settings.guard_enabled: + guard_result = await prompt_guard.check(result) + if not guard_result.safe: + calls = [tc.tool for tc in guard_result.tool_calls] + logger.warning( + "Prompt injection detected in fetched content from %s — tool calls: %s", + url, + calls, + ) + raise ValueError( + f"Prompt injection detected in content from {url}. 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