c29f13fd69
Introduces scan mode selection to control testing depth and methodology: - quick: optimized for CI/CD, focuses on recent changes and high-impact vulns - standard: balanced coverage with systematic methodology - deep: exhaustive testing with hierarchical agent swarm (now default) Each mode has dedicated prompt modules with detailed pentesting guidelines covering reconnaissance, mapping, business logic analysis, exploitation, and vulnerability chaining strategies. Closes #152
520 lines
19 KiB
Python
520 lines
19 KiB
Python
import logging
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import os
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from dataclasses import dataclass
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from enum import Enum
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from fnmatch import fnmatch
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from pathlib import Path
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from typing import Any
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import litellm
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from jinja2 import (
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Environment,
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FileSystemLoader,
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select_autoescape,
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)
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from litellm import ModelResponse, completion_cost
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from litellm.utils import supports_prompt_caching, supports_vision
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from strix.llm.config import LLMConfig
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from strix.llm.memory_compressor import MemoryCompressor
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from strix.llm.request_queue import get_global_queue
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from strix.llm.utils import _truncate_to_first_function, parse_tool_invocations
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from strix.prompts import load_prompt_modules
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from strix.tools import get_tools_prompt
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logger = logging.getLogger(__name__)
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litellm.drop_params = True
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litellm.modify_params = True
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_LLM_API_KEY = os.getenv("LLM_API_KEY")
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_LLM_API_BASE = (
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os.getenv("LLM_API_BASE")
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or os.getenv("OPENAI_API_BASE")
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or os.getenv("LITELLM_BASE_URL")
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or os.getenv("OLLAMA_API_BASE")
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)
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class LLMRequestFailedError(Exception):
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def __init__(self, message: str, details: str | None = None):
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super().__init__(message)
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self.message = message
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self.details = details
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SUPPORTS_STOP_WORDS_FALSE_PATTERNS: list[str] = [
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"o1*",
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"grok-4-0709",
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"grok-code-fast-1",
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"deepseek-r1-0528*",
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]
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REASONING_EFFORT_PATTERNS: list[str] = [
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"o1-2024-12-17",
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"o1",
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"o3",
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"o3-2025-04-16",
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"o3-mini-2025-01-31",
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"o3-mini",
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"o4-mini",
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"o4-mini-2025-04-16",
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"gemini-2.5-flash",
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"gemini-2.5-pro",
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"gpt-5*",
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"deepseek-r1-0528*",
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"claude-sonnet-4-5*",
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"claude-haiku-4-5*",
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]
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def normalize_model_name(model: str) -> str:
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raw = (model or "").strip().lower()
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if "/" in raw:
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name = raw.split("/")[-1]
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if ":" in name:
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name = name.split(":", 1)[0]
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else:
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name = raw
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if name.endswith("-gguf"):
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name = name[: -len("-gguf")]
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return name
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def model_matches(model: str, patterns: list[str]) -> bool:
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raw = (model or "").strip().lower()
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name = normalize_model_name(model)
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for pat in patterns:
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pat_l = pat.lower()
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if "/" in pat_l:
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if fnmatch(raw, pat_l):
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return True
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elif fnmatch(name, pat_l):
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return True
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return False
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class StepRole(str, Enum):
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AGENT = "agent"
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USER = "user"
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SYSTEM = "system"
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@dataclass
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class LLMResponse:
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content: str
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tool_invocations: list[dict[str, Any]] | None = None
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scan_id: str | None = None
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step_number: int = 1
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role: StepRole = StepRole.AGENT
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@dataclass
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class RequestStats:
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input_tokens: int = 0
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output_tokens: int = 0
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cached_tokens: int = 0
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cache_creation_tokens: int = 0
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cost: float = 0.0
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requests: int = 0
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failed_requests: int = 0
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def to_dict(self) -> dict[str, int | float]:
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return {
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"input_tokens": self.input_tokens,
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"output_tokens": self.output_tokens,
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"cached_tokens": self.cached_tokens,
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"cache_creation_tokens": self.cache_creation_tokens,
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"cost": round(self.cost, 4),
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"requests": self.requests,
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"failed_requests": self.failed_requests,
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}
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class LLM:
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def __init__(
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self, config: LLMConfig, agent_name: str | None = None, agent_id: str | None = None
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):
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self.config = config
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self.agent_name = agent_name
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self.agent_id = agent_id
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self._total_stats = RequestStats()
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self._last_request_stats = RequestStats()
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self.memory_compressor = MemoryCompressor(
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model_name=self.config.model_name,
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timeout=self.config.timeout,
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)
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if agent_name:
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prompt_dir = Path(__file__).parent.parent / "agents" / agent_name
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prompts_dir = Path(__file__).parent.parent / "prompts"
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loader = FileSystemLoader([prompt_dir, prompts_dir])
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self.jinja_env = Environment(
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loader=loader,
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autoescape=select_autoescape(enabled_extensions=(), default_for_string=False),
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)
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try:
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modules_to_load = list(self.config.prompt_modules or [])
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modules_to_load.append(f"scan_modes/{self.config.scan_mode}")
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prompt_module_content = load_prompt_modules(modules_to_load, self.jinja_env)
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def get_module(name: str) -> str:
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return prompt_module_content.get(name, "")
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self.jinja_env.globals["get_module"] = get_module
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self.system_prompt = self.jinja_env.get_template("system_prompt.jinja").render(
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get_tools_prompt=get_tools_prompt,
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loaded_module_names=list(prompt_module_content.keys()),
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**prompt_module_content,
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)
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except (FileNotFoundError, OSError, ValueError) as e:
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logger.warning(f"Failed to load system prompt for {agent_name}: {e}")
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self.system_prompt = "You are a helpful AI assistant."
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else:
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self.system_prompt = "You are a helpful AI assistant."
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def set_agent_identity(self, agent_name: str | None, agent_id: str | None) -> None:
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if agent_name:
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self.agent_name = agent_name
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if agent_id:
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self.agent_id = agent_id
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def _build_identity_message(self) -> dict[str, Any] | None:
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if not (self.agent_name and str(self.agent_name).strip()):
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return None
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identity_name = self.agent_name
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identity_id = self.agent_id
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content = (
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"\n\n"
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"<agent_identity>\n"
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"<meta>Internal metadata: do not echo or reference; "
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"not part of history or tool calls.</meta>\n"
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"<note>You are now assuming the role of this agent. "
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"Act strictly as this agent and maintain self-identity for this step. "
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"Now go answer the next needed step!</note>\n"
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f"<agent_name>{identity_name}</agent_name>\n"
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f"<agent_id>{identity_id}</agent_id>\n"
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"</agent_identity>\n\n"
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)
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return {"role": "user", "content": content}
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def _add_cache_control_to_content(
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self, content: str | list[dict[str, Any]]
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) -> str | list[dict[str, Any]]:
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if isinstance(content, str):
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return [{"type": "text", "text": content, "cache_control": {"type": "ephemeral"}}]
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if isinstance(content, list) and content:
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last_item = content[-1]
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if isinstance(last_item, dict) and last_item.get("type") == "text":
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return content[:-1] + [{**last_item, "cache_control": {"type": "ephemeral"}}]
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return content
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def _is_anthropic_model(self) -> bool:
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if not self.config.model_name:
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return False
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model_lower = self.config.model_name.lower()
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return any(provider in model_lower for provider in ["anthropic/", "claude"])
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def _calculate_cache_interval(self, total_messages: int) -> int:
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if total_messages <= 1:
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return 10
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max_cached_messages = 3
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non_system_messages = total_messages - 1
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interval = 10
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while non_system_messages // interval > max_cached_messages:
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interval += 10
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return interval
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def _prepare_cached_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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if (
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not self.config.enable_prompt_caching
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or not supports_prompt_caching(self.config.model_name)
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or not messages
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):
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return messages
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if not self._is_anthropic_model():
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return messages
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cached_messages = list(messages)
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if cached_messages and cached_messages[0].get("role") == "system":
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system_message = cached_messages[0].copy()
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system_message["content"] = self._add_cache_control_to_content(
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system_message["content"]
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)
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cached_messages[0] = system_message
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total_messages = len(cached_messages)
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if total_messages > 1:
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interval = self._calculate_cache_interval(total_messages)
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cached_count = 0
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for i in range(interval, total_messages, interval):
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if cached_count >= 3:
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break
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if i < len(cached_messages):
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message = cached_messages[i].copy()
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message["content"] = self._add_cache_control_to_content(message["content"])
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cached_messages[i] = message
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cached_count += 1
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return cached_messages
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async def generate( # noqa: PLR0912, PLR0915
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self,
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conversation_history: list[dict[str, Any]],
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scan_id: str | None = None,
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step_number: int = 1,
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) -> LLMResponse:
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messages = [{"role": "system", "content": self.system_prompt}]
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identity_message = self._build_identity_message()
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if identity_message:
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messages.append(identity_message)
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compressed_history = list(self.memory_compressor.compress_history(conversation_history))
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conversation_history.clear()
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conversation_history.extend(compressed_history)
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messages.extend(compressed_history)
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cached_messages = self._prepare_cached_messages(messages)
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try:
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response = await self._make_request(cached_messages)
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self._update_usage_stats(response)
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content = ""
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if (
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response.choices
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and hasattr(response.choices[0], "message")
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and response.choices[0].message
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):
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content = getattr(response.choices[0].message, "content", "") or ""
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content = _truncate_to_first_function(content)
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if "</function>" in content:
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function_end_index = content.find("</function>") + len("</function>")
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content = content[:function_end_index]
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tool_invocations = parse_tool_invocations(content)
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return LLMResponse(
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scan_id=scan_id,
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step_number=step_number,
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role=StepRole.AGENT,
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content=content,
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tool_invocations=tool_invocations if tool_invocations else None,
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)
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except litellm.RateLimitError as e:
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raise LLMRequestFailedError("LLM request failed: Rate limit exceeded", str(e)) from e
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except litellm.AuthenticationError as e:
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raise LLMRequestFailedError("LLM request failed: Invalid API key", str(e)) from e
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except litellm.NotFoundError as e:
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raise LLMRequestFailedError("LLM request failed: Model not found", str(e)) from e
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except litellm.ContextWindowExceededError as e:
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raise LLMRequestFailedError("LLM request failed: Context too long", str(e)) from e
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except litellm.ContentPolicyViolationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: Content policy violation", str(e)
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) from e
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except litellm.ServiceUnavailableError as e:
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raise LLMRequestFailedError("LLM request failed: Service unavailable", str(e)) from e
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except litellm.Timeout as e:
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raise LLMRequestFailedError("LLM request failed: Request timed out", str(e)) from e
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except litellm.UnprocessableEntityError as e:
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raise LLMRequestFailedError("LLM request failed: Unprocessable entity", str(e)) from e
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except litellm.InternalServerError as e:
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raise LLMRequestFailedError("LLM request failed: Internal server error", str(e)) from e
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except litellm.APIConnectionError as e:
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raise LLMRequestFailedError("LLM request failed: Connection error", str(e)) from e
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except litellm.UnsupportedParamsError as e:
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raise LLMRequestFailedError("LLM request failed: Unsupported parameters", str(e)) from e
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except litellm.BudgetExceededError as e:
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raise LLMRequestFailedError("LLM request failed: Budget exceeded", str(e)) from e
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except litellm.APIResponseValidationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: Response validation error", str(e)
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) from e
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except litellm.JSONSchemaValidationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: JSON schema validation error", str(e)
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) from e
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except litellm.InvalidRequestError as e:
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raise LLMRequestFailedError("LLM request failed: Invalid request", str(e)) from e
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except litellm.BadRequestError as e:
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raise LLMRequestFailedError("LLM request failed: Bad request", str(e)) from e
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except litellm.APIError as e:
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raise LLMRequestFailedError("LLM request failed: API error", str(e)) from e
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except litellm.OpenAIError as e:
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raise LLMRequestFailedError("LLM request failed: OpenAI error", str(e)) from e
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except Exception as e:
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raise LLMRequestFailedError(f"LLM request failed: {type(e).__name__}", str(e)) from e
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@property
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def usage_stats(self) -> dict[str, dict[str, int | float]]:
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return {
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"total": self._total_stats.to_dict(),
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"last_request": self._last_request_stats.to_dict(),
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}
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def get_cache_config(self) -> dict[str, bool]:
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return {
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"enabled": self.config.enable_prompt_caching,
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"supported": supports_prompt_caching(self.config.model_name),
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}
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def _should_include_stop_param(self) -> bool:
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if not self.config.model_name:
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return True
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return not model_matches(self.config.model_name, SUPPORTS_STOP_WORDS_FALSE_PATTERNS)
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def _should_include_reasoning_effort(self) -> bool:
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if not self.config.model_name:
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return False
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return model_matches(self.config.model_name, REASONING_EFFORT_PATTERNS)
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def _model_supports_vision(self) -> bool:
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if not self.config.model_name:
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return False
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try:
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return bool(supports_vision(model=self.config.model_name))
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except Exception: # noqa: BLE001
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return False
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def _filter_images_from_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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filtered_messages = []
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for msg in messages:
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content = msg.get("content")
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updated_msg = msg
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if isinstance(content, list):
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filtered_content = []
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for item in content:
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if isinstance(item, dict):
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if item.get("type") == "image_url":
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filtered_content.append(
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{
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"type": "text",
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"text": "[Screenshot removed - model does not support "
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"vision. Use view_source or execute_js instead.]",
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}
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)
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else:
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filtered_content.append(item)
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else:
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filtered_content.append(item)
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if filtered_content:
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text_parts = [
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item.get("text", "") if isinstance(item, dict) else str(item)
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for item in filtered_content
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]
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all_text = all(
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isinstance(item, dict) and item.get("type") == "text"
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for item in filtered_content
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)
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if all_text:
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updated_msg = {**msg, "content": "\n".join(text_parts)}
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else:
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updated_msg = {**msg, "content": filtered_content}
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else:
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updated_msg = {**msg, "content": ""}
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filtered_messages.append(updated_msg)
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return filtered_messages
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async def _make_request(
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self,
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messages: list[dict[str, Any]],
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) -> ModelResponse:
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if not self._model_supports_vision():
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messages = self._filter_images_from_messages(messages)
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completion_args: dict[str, Any] = {
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"model": self.config.model_name,
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"messages": messages,
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"timeout": self.config.timeout,
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}
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if _LLM_API_KEY:
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completion_args["api_key"] = _LLM_API_KEY
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if _LLM_API_BASE:
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completion_args["api_base"] = _LLM_API_BASE
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if self._should_include_stop_param():
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completion_args["stop"] = ["</function>"]
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if self._should_include_reasoning_effort():
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completion_args["reasoning_effort"] = "high"
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queue = get_global_queue()
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response = await queue.make_request(completion_args)
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self._total_stats.requests += 1
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self._last_request_stats = RequestStats(requests=1)
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return response
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def _update_usage_stats(self, response: ModelResponse) -> None:
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try:
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if hasattr(response, "usage") and response.usage:
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input_tokens = getattr(response.usage, "prompt_tokens", 0)
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output_tokens = getattr(response.usage, "completion_tokens", 0)
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cached_tokens = 0
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cache_creation_tokens = 0
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if hasattr(response.usage, "prompt_tokens_details"):
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prompt_details = response.usage.prompt_tokens_details
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if hasattr(prompt_details, "cached_tokens"):
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cached_tokens = prompt_details.cached_tokens or 0
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if hasattr(response.usage, "cache_creation_input_tokens"):
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cache_creation_tokens = response.usage.cache_creation_input_tokens or 0
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else:
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input_tokens = 0
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output_tokens = 0
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cached_tokens = 0
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cache_creation_tokens = 0
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try:
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cost = completion_cost(response) or 0.0
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except Exception as e: # noqa: BLE001
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logger.warning(f"Failed to calculate cost: {e}")
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cost = 0.0
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|
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self._total_stats.input_tokens += input_tokens
|
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self._total_stats.output_tokens += output_tokens
|
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self._total_stats.cached_tokens += cached_tokens
|
|
self._total_stats.cache_creation_tokens += cache_creation_tokens
|
|
self._total_stats.cost += cost
|
|
|
|
self._last_request_stats.input_tokens = input_tokens
|
|
self._last_request_stats.output_tokens = output_tokens
|
|
self._last_request_stats.cached_tokens = cached_tokens
|
|
self._last_request_stats.cache_creation_tokens = cache_creation_tokens
|
|
self._last_request_stats.cost = cost
|
|
|
|
if cached_tokens > 0:
|
|
logger.info(f"Cache hit: {cached_tokens} cached tokens, {input_tokens} new tokens")
|
|
if cache_creation_tokens > 0:
|
|
logger.info(f"Cache creation: {cache_creation_tokens} tokens written to cache")
|
|
|
|
logger.info(f"Usage stats: {self.usage_stats}")
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning(f"Failed to update usage stats: {e}")
|