AI Pentesting Copilots
AI pentesting copilots assist human testers with analysis, tool orchestration, and reporting — they make testers faster and more consistent, not redundant.
How AI Copilots Work
AI copilots sit between the pentester and their toolchain. They accept natural language queries, translate them into tool invocations, analyze results, and present findings with actionable context.
AI Copilot Workflow
Scroll sideways to see the whole diagram
| Technique | What it does | Tool/prompt | Detection signal |
|---|---|---|---|
| Query translation | Turns natural-language queries into tool invocations | Claude + MCP security server; function-calling loop | Log of AI-initiated tool executions |
| Result triage | Analyzes scan output and ranks findings with context | Low-temperature model (0.1–0.3); truncated outputs | Manual verification of AI severity ratings |
| Next-step suggestion | Proposes follow-up commands from current findings | Read-only analysis before active scanning | Scope re-verification before each scan |
| Report drafting | Drafts findings with severity and remediation | PlexTrac / AttackForge; custom copilot script | Human review; no client data to cloud AI without approval |
Autonomous & Agentic Pentesting Platforms (2025–2026)
Autonomous pentest platforms — XBOW, Bugcrowd's copilot, HackerOne Hai, and the Mindgard / Lakera class of AI-system red-teamers — are compared in depth in the Autonomous AI Pentest Platforms guide (autonomy, data handling, and evidence quality). Use this page for the copilot workflow and that page for platform selection; this section keeps only the summary to avoid drift.
AI-Assisted Code-Review & SAST Copilots
ZeroPath
AI-native SAST. Goes beyond pattern matching by reasoning about cross-file data flow with an LLM; documented real-world CVE finds in 2024–2025. Pairs well with manual review during white-box engagements.
Corgea
AI SAST + auto-remediation. Generates fix PRs for the bugs it finds; useful to demonstrate \"developer-actionable\" findings to clients during a pentest.
Snyk Agent Fix / GitHub Copilot Autofix
Platform-native AI code review. Useful as a reference signal during engagements where the client already runs them — diff their output against your manual findings.
Semgrep Pro / SonarQube Cloud (AI assist)
Established SAST products that added LLM rule-suggestion and triage layers in 2024–2025. Good baseline to combine with ZeroPath / Corgea for cross-tool corroboration.
Adjacent IDE-side coding agents pentesters use daily
These are not pentest products per se, but they have become the default exploit-development / payload-tweaking environment in 2025–2026. Use them inside a sandbox and follow the vibe-coding security guidance to avoid being attacked through your own tooling.
- Cursor (Composer / Agent) — multi-file edits + shell tools; great for rapid PoC scripting in Python / Go / Rust.
- Windsurf (Cascade) — agentic IDE with strong context handling; popular for Burp extension dev and exploit scripting.
- Claude Code (CLI) — Anthropic's terminal coding agent; ideal for one-shot \"write me a binary patcher / shellcode encoder\" tasks.
- Codex CLI (OpenAI) — terminal agent with sandboxed exec; good for fast Burp / Caido plugin scaffolding.
- GitHub Copilot Chat (agent mode) — Visual Studio & VS Code; useful for in-repo refactors during source-code review engagements.
- Gemini Code Assist (Enterprise) — useful when the engagement is in a Google Cloud / Workspace environment with private context.
- Aider — open-source pair-programming CLI; works well with local models (Ollama / vLLM) when the engagement requires keeping all code on-prem.
HackerAI, BurpGPT & PentestGPT
Pentest Copilot (Bugcrowd)
Bugcrowd Pentest Copilot
bugcrowd.com — Commercial Platform
AI assistant in the Bugcrowd platform for vulnerability triage, deduplication, and report quality. Facts in the comparison table below.
Vulnerability Analysis
Automatically analyzes submitted findings, validates severity ratings, and suggests CVSS vector strings based on the vulnerability description and proof of concept.
Finding Deduplication
AI-powered deduplication identifies when new submissions overlap with existing findings, reducing triage time and preventing duplicate payouts.
Report Writing Assist
Generates professional vulnerability write-ups from minimal input — provide a title and PoC, and the AI drafts a complete finding with impact analysis and remediation steps.
Scope Analysis
Parses program scope definitions and helps researchers quickly determine whether a target or vulnerability class is in-scope before investing testing time.
Caido AI
Caido
caido.io — Modern Web Proxy + AI
Rust web proxy (caido.io); the AI Assistant is a paid in-app add-on under Settings → AI. Facts in the comparison table below.
Installation & Setup
# Install Caido — download the desktop app or CLI from https://caido.io/download
# Builds are available for Linux, macOS, and Windows.
# The command-line binary is caido-cli (the desktop app embeds it)
caido-cli --version
caido-cli --help
# Caido's configuration file is caido.yamlAI Assistant is a paid in-app feature
caido-cli, the configuration file is
caido.yaml, and the Assistant is set up under Settings → AI inside the
app. See the
Caido AI Assistant quickstart.
Traffic Analysis
AI flags suspicious responses, auth issues, and data leaks in proxied traffic
Param Discovery
AI-powered hidden parameter detection beyond traditional wordlists
Smart Replay
Intelligently mutates requests to test for injection, auth bypass, and IDOR
BurpGPT & ReconAIzer
Burp Suite AI Extensions
BurpGPT (aress31) + ReconAIzer (hisxo)
Burp Suite extensions for LLM analysis inside the proxy: BurpGPT (passive scanning, Pro only — Community unmaintained) and ReconAIzer (context-menu traffic analysis). Facts in the comparison table below.
BurpGPT Community is no longer maintained
Setup & Configuration
# BurpGPT — the Community edition is no longer maintained upstream.
# Only BurpGPT Pro is supported; see github.com/aress31/burpgpt for
# current install and licensing details.
# 1. Install BurpGPT Pro per the upstream instructions
# 2. Configure in Burp → Extensions → BurpGPT → Settings
# Set provider: OpenAI / Azure OpenAI / Local (Ollama)
# Set the current model and API key, then save
# ReconAIzer — Install from BApp Store or GitHub
# 1. Burp → Extensions → Add → Select reconaizer.jar
# 2. Set OpenAI API key in extension tab
# 3. Right-click any request → "Send to ReconAIzer"
# Custom prompt template (BurpGPT)
# Analyze the following HTTP request/response pair.
# Identify: SQLi, XSS, SSRF, IDOR, auth bypass, info disclosure.
# Rate severity: Critical / High / Medium / Low / Info.
# Provide proof-of-concept payloads where applicable.BurpGPT Features
- • Passive scanning with GPT analysis of every proxied request
- • Custom prompt templates for specific vuln classes
- • Batch analysis of site map or proxy history
- • Export AI findings to Burp issue tracker
- • Support for OpenAI, Azure, and local models
ReconAIzer Features
- • Right-click context menu → "Analyze with AI"
- • Automatic HTTP request/response analysis
- • Payload suggestions based on detected parameters
- • Sensitive data detection in responses
- • Security misconfiguration identification
API Cost Optimization
HackerAI
HackerAI
hackerai.co — Specialized Security AI (formerly HackerGPT)
Security assistant at hackerai.co (formerly HackerGPT) for recon, payloads, and report drafting. Facts in the comparison table below.
# HackerAI (formerly HackerGPT) — hackerai.co
# No installation needed — web-based interface
# Free tier: limited daily messages
# Pro tier: higher limits + tool integrations
# Supported workflows:
# • Bug bounty recon assistance
# • Vulnerability report drafting
# • Payload generation & encoding
# • Tool command reference (nmap, ffuf, sqlmap, etc.)
# • CVE analysis & exploit guidance
# • OWASP Top 10 testing checklists
# Example prompt:
# "Analyze this HTTP response for IDOR vulnerabilities
# and suggest bypass techniques for the UUID-based
# access control on /api/v2/users/{id}/documents"Bug Bounty Focus
Trained on bug bounty reports, HackerOne disclosures, and security research
Report Writing
Generates professional vulnerability reports with impact and PoC sections
Free Tier
Free tier available — no API key or setup required; paid tiers raise limits
PlexTrac AI
PlexTrac
plextrac.com — AI-Assisted Pentest Reporting
Commercial pentest reporting platform; AI writing, risk scoring, and exec summaries run from the web UI against your own tenant. Facts in the comparison table below.
# PlexTrac AI — pentest reporting platform
# Access: your own tenant, e.g. https://<your-instance>.plextrac.com
# (there is no shared central API host — each tenant has its own URL)
# Auth: authenticate against YOUR tenant to obtain a JWT, then call the
# same tenant's REST API with it:
# POST https://<your-instance>.plextrac.com/api/v1/authenticate
# GET https://<your-instance>.plextrac.com/api/v2/...
# Finding payload fields are documented per tenant; there are no
# "ai_enhance" / "auto_remediation" request fields. AI writing,
# risk scoring, deduplication and exec summaries are driven from
# the web UI, not by API request flags.
# See the vendor docs: https://docs.plextrac.comWritingAssist
Auto-generates complete finding descriptions from minimal input. Provide a vulnerability title and basic context — AI produces description, impact, steps to reproduce, and remediation.
AI Risk Scoring
Combines CVSS base scores with business context to produce risk ratings that account for asset criticality, data sensitivity, and compensating controls.
AttackForge AI
AttackForge
attackforge.com — AI Pentest Management
Pentest management platform; correlation, reporting, and triage run from the web UI against your own tenant. Facts in the comparison table below.
# AttackForge — pentest management platform
# Access: your own tenant, e.g. https://<your-instance>.attackforge.com
# AI-assisted features (finding correlation, report generation, triage
# and prioritisation, risk trend analysis) are driven from the web UI.
# Programmatic access uses the tenant's documented REST/OpenAPI
# endpoints with a tenant-scoped token. There is no documented
# "af-cli" binary, and no --ai-enhance / --auto-correlate flags.
# See your tenant's API documentation / OpenAPI spec for exact
# routes and payloads.Vuln Correlation
AI correlates findings across engagements to identify systemic issues
Auto Reports
Generate complete pentest reports from findings with one click
Workflow AI
Smart task assignment and progress tracking across team members
Building Custom AI Copilots
Commercial copilots are powerful, but building your own gives you full control over tool integration, prompt engineering, and data privacy. Below are three approaches ranging from cloud to fully local.
Authorization Required
Approach 1: Claude with MCP Tools
The Model Context Protocol (MCP) lets Claude Desktop or VS Code Copilot directly invoke security tools — see the MCP Security guide for the threat model before connecting servers. See the HexStrike AI guide for a full MCP server with 150+ tools.
# Claude with MCP Tools — leveraging HexStrike or custom MCP servers
# See: /offensive-ai/02-hexstrike/ for full MCP setup
# claude_desktop_config.json
{
"mcpServers": {
"security-copilot": {
"command": "python3",
"args": ["./mcp_security_server.py"],
"env": {
"NMAP_PATH": "/usr/bin/nmap",
"NUCLEI_PATH": "/usr/bin/nuclei",
"WORDLISTS": "/usr/share/seclists"
}
}
}
}
# With MCP, Claude can directly:
# • Run nmap/nuclei/ffuf scans
# • Parse and correlate results
# • Generate findings with CVSS scores
# • Write complete pentest reports
# • Chain tools in multi-step workflowsApproach 2: GPT-5.x with Function Calling
OpenAI's function calling API lets you define security tools as typed functions that the model can invoke in a loop — building an autonomous pentest pipeline with human oversight.
#!/usr/bin/env python3
"""AI Security Copilot — Custom implementation using OpenAI function calling."""
import json
import subprocess
import openai
client = openai.OpenAI() # Uses OPENAI_API_KEY env var
# Define security tools the AI can invoke
TOOLS = [
{
"type": "function",
"function": {
"name": "nmap_scan",
"description": "Run an Nmap scan against a target host or network",
"parameters": {
"type": "object",
"properties": {
"target": {"type": "string", "description": "Target IP or hostname"},
"scan_type": {
"type": "string",
"enum": ["-sV", "-sC", "-sS", "-A", "-O"],
"description": "Nmap scan type flag"
},
"ports": {"type": "string", "description": "Port range, e.g. 1-1000"}
},
"required": ["target"]
}
}
},
{
"type": "function",
"function": {
"name": "nuclei_scan",
"description": "Run Nuclei vulnerability scanner with templates",
"parameters": {
"type": "object",
"properties": {
"target": {"type": "string", "description": "Target URL"},
"templates": {
"type": "string",
"description": "Template tags: cves,misconfig,exposures,takeovers"
},
"severity": {
"type": "string",
"enum": ["info", "low", "medium", "high", "critical"],
"description": "Minimum severity filter"
}
},
"required": ["target"]
}
}
},
{
"type": "function",
"function": {
"name": "ffuf_fuzz",
"description": "Run ffuf directory/parameter fuzzing",
"parameters": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "Target URL with FUZZ keyword"},
"wordlist": {"type": "string", "description": "Path to wordlist"},
"method": {"type": "string", "enum": ["GET", "POST"], "default": "GET"}
},
"required": ["url"]
}
}
},
{
"type": "function",
"function": {
"name": "analyze_response",
"description": "Analyze an HTTP response for security issues",
"parameters": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "URL to fetch and analyze"},
"check_headers": {"type": "boolean", "default": True},
"check_cookies": {"type": "boolean", "default": True}
},
"required": ["url"]
}
}
}
]
def execute_tool(name: str, args: dict) -> str:
"""Execute a security tool and return output."""
if name == "nmap_scan":
cmd = ["nmap", args.get("scan_type", "-sV")]
if "ports" in args:
cmd += ["-p", args["ports"]]
cmd.append(args["target"])
result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
return result.stdout
elif name == "nuclei_scan":
cmd = ["nuclei", "-u", args["target"], "-silent"]
if "templates" in args:
cmd += ["-tags", args["templates"]]
if "severity" in args:
cmd += ["-severity", args["severity"]]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
return result.stdout
elif name == "ffuf_fuzz":
wordlist = args.get("wordlist", "/usr/share/seclists/Discovery/Web-Content/common.txt")
cmd = ["ffuf", "-u", args["url"], "-w", wordlist, "-mc", "200,301,302,403"]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
return result.stdout
elif name == "analyze_response":
import requests
resp = requests.get(args["url"], verify=False, timeout=10)
analysis = f"Status: {resp.status_code}\nHeaders:\n"
security_headers = [
"Content-Security-Policy", "X-Frame-Options",
"X-Content-Type-Options", "Strict-Transport-Security",
"X-XSS-Protection", "Referrer-Policy"
]
for h in security_headers:
val = resp.headers.get(h, "MISSING")
analysis += f" {h}: {val}\n"
return analysis
return f"Unknown tool: {name}"
def run_copilot(user_message: str, target_context: str = "") -> None:
"""Run the AI security copilot with tool-calling loop."""
system_prompt = """You are an expert penetration tester AI copilot.
You have access to security tools: nmap, nuclei, ffuf, and HTTP analysis.
Plan your approach methodically:
1. Enumerate the attack surface
2. Identify potential vulnerabilities
3. Validate findings with targeted scans
4. Provide actionable findings with severity ratings
Always explain your reasoning before executing tools."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Target context: {target_context}\n\n{user_message}"}
]
while True:
response = client.chat.completions.create(
model="gpt-4o",
messages=messages,
tools=TOOLS,
tool_choice="auto"
)
msg = response.choices[0].message
messages.append(msg)
if msg.tool_calls:
for tc in msg.tool_calls:
args = json.loads(tc.function.arguments)
print(f"[*] Executing: {tc.function.name}({args})")
result = execute_tool(tc.function.name, args)
messages.append({
"role": "tool",
"tool_call_id": tc.id,
"content": result[:8000] # Truncate large outputs
})
else:
print(msg.content)
break
if __name__ == "__main__":
run_copilot(
"Perform a security assessment of the target web application.",
target_context="Target: 10.10.10.50, Scope: *.target.htb, Auth: unauthenticated"
)Approach 3: Local Models with Ollama
For air-gapped environments or data-sensitive engagements, run a fully local copilot using Ollama. No API keys, no data exfiltration risk, no cloud dependency.
#!/usr/bin/env python3
"""Local AI Security Copilot using Ollama — no API keys needed."""
import json
import subprocess
import requests
OLLAMA_URL = "http://localhost:11434/api/chat"
MODEL = "llama3.1:70b" # Or mixtral, codestral, etc.
def chat_with_tools(messages: list) -> dict:
"""Send messages to local Ollama model."""
resp = requests.post(OLLAMA_URL, json={
"model": MODEL,
"messages": messages,
"stream": False,
"options": {"temperature": 0.1, "num_ctx": 32768}
})
return resp.json()["message"]
def security_copilot(query: str) -> None:
"""Offline security copilot — runs entirely on local hardware."""
system = """You are a penetration testing assistant running locally.
Analyze security findings, suggest next steps, and help write reports.
You have no internet access — focus on analysis and methodology."""
messages = [
{"role": "system", "content": system},
{"role": "user", "content": query}
]
response = chat_with_tools(messages)
print(response["content"])
# Usage:
# security_copilot("Analyze these nmap results and suggest attack vectors: ...")Copilot Comparison
| Tool | Type | Best For | AI Model | Cost | Open Source? |
|---|---|---|---|---|---|
| Pentest Copilot | Platform | Bug bounty triage | Proprietary | Bugcrowd subscription | No |
| Caido AI | Web Proxy | Web app testing | Configurable (in-app) | Free / Pro; AI is a paid add-on | Partial (core) |
| BurpGPT | Burp Extension | Passive scanning | Current OpenAI / Local | BurpGPT Pro licence + API costs | No (Pro is commercial) |
| ReconAIzer | Burp Extension | Traffic analysis | Configurable | API costs | Yes |
| HackerAI | Web App | Bug bounty, learning | Frontier models | Free + Pro | No |
| PlexTrac AI | Reporting Platform | Report automation | Proprietary | Commercial license | No |
| AttackForge AI | Management Platform | Pentest ops | Proprietary | Commercial license | No |
| Custom (Cloud API) | DIY Script | Full control | Any (OpenAI, Claude) | API costs | Your code |
| Custom (Ollama) | DIY Local | Air-gapped / private | Llama 4, Qwen3 | Free (hardware) | Yes |
Best Practices
Do
- • Verify AI findings manually — false positives are common
- • Use low temperature (0.1–0.3) for tool-calling copilots
- • Truncate large tool outputs to stay within context limits
- • Log all AI-initiated tool executions for audit trails
- • Start with read-only analysis before enabling active scanning
Don't
- • Blindly trust AI severity ratings without validation
- • Send client data to cloud AI without contract approval
- • Let AI copilots run unattended against production systems
- • Use AI-generated PoCs without understanding them first
- • Skip scope verification because "the AI said it was fine"
Data Privacy
Practice Labs
Project Links
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