AI-automated penetration testing and general problem-solving system that achieved unique AK (All Killed) in Tencent Cloud Hackathon intelligent penetration challenge
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AI-automated penetration testing and general problem-solving system that achieved unique AK (All Killed) in Tencent Cloud Hackathon intelligent penetration challenge
triggers
["set up cairn for automated penetration testing","use cairn AI pentest agent","configure cairn for security testing","run cairn against a target","cairn hackathon pentest system","automate vulnerability discovery with cairn","cairn AI problem solving agent","integrate cairn into my security workflow"]
Cairn is an AI-driven automated penetration testing and general problem-solving framework developed by the Bytex@่ตท้ถ่ก่ฟนๅฎ้ชๅฎค team. It achieved the unique "AK" (All Killed / full score) result in the 2nd TCH Tencent Cloud Hackathon Intelligent Penetration Challenge, placing 4th online. The system uses LLM-based agents to autonomously reason about, plan, and execute multi-step security testing tasks.
What Cairn Does
Autonomous AI Agent Loop: Iteratively reasons about a target, selects tools, executes commands, and interprets results
Penetration Testing Automation: Web vulnerability discovery, exploitation, CTF-style challenge solving
General Problem Solving: Extensible to non-security tasks via tool/plugin architecture
Multi-step Planning: Breaks complex objectives into subtasks with memory and context management
Tool Integration: Wraps common pentest tools (nmap, sqlmap, curl, custom scripts) as callable agent actions
Project Status
โ ๏ธ Code is still being organized and is expected to be open-sourced soon. The examples below reflect the architecture described in the competition writeup and visible repository structure.
# Run against a CTF challenge or target
python cairn.py --target "http://192.168.1.100" --goal "Find and exploit SQL injection to retrieve admin credentials"# With custom model
python cairn.py --target "http://challenge.example.com" \
--goal "Solve this web CTF challenge and get the flag" \
--model gpt-4o \
--max-iterations 25
# Dry run (plan only, no execution)
python cairn.py --target "http://192.168.1.100" \
--goal "Enumerate all open services" \
--dry-run
2. Python API Usage (Expected)
from cairn import CairnAgent
from cairn.tools import ToolRegistry
from cairn.config import CairnConfig
# Initialize configuration
config = CairnConfig(
model_name="gpt-4o",
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1"),
max_iterations=30,
target_scope=["192.168.1.0/24"],
)
# Build tool registry
tools = ToolRegistry()
tools.register_defaults() # nmap, curl, sqlmap, ffuf, etc.# Create and run agent
agent = CairnAgent(config=config, tools=tools)
result = agent.run(
target="http://192.168.1.100",
goal="Find all web vulnerabilities and attempt exploitation",
)
print(result.summary)
print(result.findings)
3. Custom Tool Registration
from cairn.tools import Tool, ToolResult
classCustomExploitTool(Tool):
name = "custom_exploit"
description = "Exploits a specific vulnerability in target application"defexecute(self, target: str, payload: str, **kwargs) -> ToolResult:
import subprocess
cmd = f"python exploit.py --target {target} --payload '{payload}'"
output = subprocess.run(cmd, shell=True, capture_output=True, text=True)
return ToolResult(
success=output.returncode == 0,
output=output.stdout,
error=output.stderr,
)
# Register with agent
tools.register(CustomExploitTool())
agent = CairnAgent(config=config, tools=tools)
4. Multi-Phase Penetration Test
from cairn import CairnAgent, Phase
from cairn.pipeline import PentestPipeline
pipeline = PentestPipeline(agent=agent)
# Define phases
pipeline.add_phase(Phase(
name="reconnaissance",
goal="Enumerate all open ports and services on {target}",
))
pipeline.add_phase(Phase(
name="vulnerability_scan",
goal="Based on discovered services, identify exploitable vulnerabilities",
depends_on=["reconnaissance"],
))
pipeline.add_phase(Phase(
name="exploitation",
goal="Exploit identified vulnerabilities and achieve {objective}",
depends_on=["vulnerability_scan"],
))
# Run full pipeline
report = pipeline.run(
target="192.168.1.100",
objective="obtain root shell or read /flag",
)
report.save("./reports/pentest_report.json")
from cairn.memory import AgentMemory
# Memory persists findings across agent steps
memory = AgentMemory(
short_term_limit=20, # Recent observations in context
long_term_enabled=True, # Summarize older context
facts_store=True, # Extract and index key facts
)
agent = CairnAgent(config=config, tools=tools, memory=memory)
# Access collected facts after runfor finding in agent.memory.findings:
print(f"[{finding.severity}] {finding.description}")
print(f" Evidence: {finding.evidence}")
print(f" Recommendation: {finding.remediation}")
Configuration Reference
# cairn/config.py (expected structure)@dataclassclassCairnConfig:
# LLM settings
model_name: str = "gpt-4o"
api_key: str = field(default_factory=lambda: os.environ["OPENAI_API_KEY"])
base_url: str = "https://api.openai.com/v1"
temperature: float = 0.1# Low temp for consistent tool use
max_tokens: int = 4096# Agent behavior
max_iterations: int = 30# Hard stop on runaway loops
timeout_per_step: int = 60# Seconds per tool execution
verbose: bool = False# Safety
target_scope: list[str] = field(default_factory=list)
dry_run: bool = False# Plan without executing
require_confirmation: bool = False# Interactive approval per step# Output
report_format: str = "json"# json | markdown | html
report_path: str = "./reports"
Prompt Engineering Patterns
Cairn uses structured system prompts for reliable tool invocation:
# Example system prompt structure (inferred from competition writeup)
SYSTEM_PROMPT = """You are an expert penetration tester AI agent.
## Objective
{goal}
## Target
{target}
## Available Tools
{tool_descriptions}
## Rules
1. Always reason step-by-step before acting
2. Stay within scope: {scope}
3. Prefer non-destructive enumeration before exploitation
4. Document every finding with evidence
## Response Format
Thought: <your reasoning>
Action: <tool_name>
Action Input: <tool parameters as JSON>
After receiving Observation, continue until you reach a Final Answer.
"""
CTF / Challenge Mode
# Optimized for CTF flag capture
python cairn.py \
--mode ctf \
--target "http://ctf-challenge.com:8080" \
--goal "Find the hidden flag in format FLAG{...}" \
--model gpt-4o \
--iterations 50 \
--verbose
# With flag pattern matching
python cairn.py \
--mode ctf \
--target "http://target.com" \
--flag-pattern "CTF\{[a-zA-Z0-9_]+\}" \
--auto-submit
Logging and Debugging
import logging
from cairn import CairnAgent
# Enable detailed agent trace logging
logging.basicConfig(level=logging.DEBUG)
agent = CairnAgent(config=config, tools=tools, verbose=True)
# Each step is logged:# [THINK] Analyzing login form for injection points...# [ACT] Calling tool: http_request# [INPUT] {"url": "...", "method": "POST", "data": {...}}# [OBS] Response 200, contains "Invalid credentials"# [THINK] Response suggests valid injection point, trying UNION...
Troubleshooting
Issue
Cause
Fix
Agent loops without progress
Goal too vague or tools failing silently
Add --max-iterations 15, use --verbose to inspect loop
Tool execution timeout
Slow network or heavy scan
Increase TIMEOUT_PER_STEP in config
LLM refuses tool call
Safety filter on model provider
Use a less restrictive model endpoint or rephrase goal
Out of context window
Long agent history
Reduce short_term_limit or enable memory summarization
Scope violation error
Target not in allowed scope
Add target CIDR to TARGET_SCOPE in .env
Empty findings report
Agent completed but found nothing
Check target accessibility, increase iterations
Responsible Use
Cairn is licensed under AGPL-3.0. Usage must comply with:
โ Authorized penetration tests with written permission
โ CTF competitions and intentionally vulnerable lab environments
โ Personal security research on systems you own
โ Unauthorized access to systems you don't own
โ Commercial use without a separate commercial license
Contact the maintainer at the repository for commercial licensing inquiries.
