mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 06:09:55 +02:00
Alexander Myasoedov
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
import os
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import httpx
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from loguru import logger
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from pydantic import BaseModel
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from langalf.probe_actor.refusal import refusal_heuristic
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from langalf.probe_data.data import prepare_prompts
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IS_VERCEL = os.getenv("IS_VERCEL", "f") == "t"
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class ScanResult(BaseModel):
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module: str
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tokens: float
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cost: float
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progress: float
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failureRate: float = 0.0
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status: bool = False
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@classmethod
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def status_msg(cls, msg: str):
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return cls(
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module=msg,
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tokens=0,
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cost=0,
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progress=0,
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failureRate=0,
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status=True,
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).json()
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async def perform_scan(request_factory, max_budget: int, datasets: list[dict] = []):
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yield ScanResult.status_msg("Loading datasets...")
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if IS_VERCEL:
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yield ScanResult.status_msg(
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"Vercel deployment detected. Streaming messages are not supported by serverless, plz run it locally."
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)
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return
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prompt_modules = prepare_prompts(
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dataset_names=[m["dataset_name"] for m in datasets if m["selected"]],
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budget=max_budget,
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)
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yield ScanResult.status_msg("Datasets loaded. Starting scan...")
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errors = []
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refusals = []
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size = sum(len(m.prompts) for m in prompt_modules)
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step = 0
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for mi, module in enumerate(prompt_modules):
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tokens = 0
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module_failures = 0
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logger.info(f"Scanning {module.dataset_name} {len(module.prompts)}")
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for i, prompt in enumerate(module.prompts):
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step += 1
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progress = 100 * (step) / size
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# Naive token count
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tokens += len(prompt.split())
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try:
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r = await request_factory.fn(prompt=prompt)
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except httpx.RequestError as e:
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logger.error(f"Request error: {e}")
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errors.append((module.dataset_name, prompt.replace("\n", ";"), e))
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module_failures += 1
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continue
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if r.status_code >= 400:
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module_failures += 1
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errors.append(
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(
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module.dataset_name,
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prompt.replace("\n", ";"),
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r.status_code,
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r.text,
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)
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)
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elif not refusal_heuristic(r.json()):
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refusals.append(
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(
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module.dataset_name,
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prompt.replace("\n", ";"),
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r.status_code,
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r.text,
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)
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)
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module_failures += 1
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# Naive token count for llm response
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tokens += len(r.text.split())
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yield ScanResult(
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module=module.dataset_name,
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tokens=round(tokens / 1000, 1),
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cost=round(tokens * 1.5 / 1000_000, 2),
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progress=round(progress, 2),
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failureRate=100 * module_failures / max(len(module.prompts), 1),
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).json()
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yield ScanResult.status_msg("Done.")
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import pandas as pd
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df = pd.DataFrame(
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errors + refusals, columns=["module", "prompt", "status_code", "content"]
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)
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df.to_csv("failures.csv", index=False)
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# TODO: save all results
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