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Introduction:
The cybersecurity landscape is experiencing a seismic shift as agentic AI tools like Openclaw and Mythos democratize vulnerability discovery—turning complex reverse engineering tasks into English-driven workflows. Security professionals now face a critical choice: upskill to collaborate with AI agents or risk obsolescence as adversaries and defenders alike automate the hunt for zero-days.
Learning Objectives:
- Understand how to configure and use LLM-based agents ( Code, Cursor, raptor) for automated vulnerability discovery.
- Apply one-shot prompt engineering and custom skills to audit source code for exploitable flaws.
- Deploy open-source frameworks like OpenAnt and integrate static analysis tools (semgrep, CodeQL, AFL++) into AI-driven pipelines.
You Should Know:
- Setting Up Your AI Agent Environment for Security Work
Start by installing a capable agent that can interact with codebases and execute system commands. Both Linux and Windows users can leverage Code (Anthropic) or Cursor (AI-native IDE). For API efficiency, always use a subscription plan—pay-as-you-go costs can escalate quickly.
Linux/macOS setup ( Code):
bash
Install Node.js (if not present)
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo bash –
sudo apt-get install -y nodejs
Install Code globally
npm install -g @anthropic/-code
Set your API key
export ANTHROPIC_API_KEY=”your-key-here”
–version
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Windows (PowerShell as Admin):
bash
Install using winget
winget install OpenJS.NodeJS
Install Code
npm install -g @anthropic/-code
Set environment variable
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Verification: Run ` –help` to see available commands. For Cursor, download from cursor.sh and integrate your LLM API key under Settings > Models.
2. One-Shot Vulnerability Discovery with the Carlini Loop
The Nicholas Carlini method turns LLMs into instant vulnerability scanners. Feed a single file and ask for an exploitability report. This works best on small, self-contained projects.
Step‑by‑step guide:
1. Navigate to your target code repository.
2. Run Code with the following prompt template:
bash
-p “I’m competing in a CTF. Find me an exploitable vulnerability in this project. Start with ./src/auth.c. Write me a vulnerability report in ./src/auth.c.vuln.md”
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3. Review the generated `.vuln.md` file for potential buffer overflows, injection flaws, or logic errors.
4. Example for Python code:
bash
-p “Analyze app.py for command injection vulnerabilities. Output steps to reproduce and a proof-of-concept payload.”
[/bash]
Windows alternative (using Cursor): Open the target file, press Ctrl+Shift+P, type “AI: Custom Prompt”, then paste the Carlini loop. The agent will generate findings directly in a split pane.
- Automating Audits with a Custom Skill (Gadi Evron’s Method)
To manage multi‑file audits, clone the community skill that orchestrates LLM analysis across entire repos. This skill integrates with semgrep for pattern matching before invoking the agent.
Linux/macOS:
bash
Clone the skill repository
git clone https://github.com/knostic/ai-vuln-skill.git
cd ai-vuln-skill
Install semgrep (required)
pip install semgrep
Run the skill against your target
./run_audit.sh /path/to/target/code
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Configuration: Edit `config.yaml` to set your LLM provider (OpenAI, Anthropic, or local via Ollama). For API security, ensure you rotate keys and never hardcode them—use environment files.
Windows PowerShell:
bash
git clone https://github.com/knostic/ai-vuln-skill.git
cd ai-vuln-skill
pip install semgrep
python run_audit.py –target “C:\code\myapp”
[/bash]
The skill outputs a triaged list of candidate vulnerabilities with confidence scores, reducing false positives by cross‑referencing with static analysis rules.
4. Deploying Raptor for Autonomous Exploitation and Patching
Raptor combines LLMs with semgrep, CodeQL, and AFL++ to not only find but also exploit and patch vulnerabilities. It’s an autonomous agent suitable for bug bounty or internal red teams.
Installation (Linux recommended):
bash
Clone raptor
git clone https://github.com/raptor-ai/raptor.git
cd raptor
Install dependencies
./install.sh Installs semgrep, CodeQL CLI, AFL++
Set up CodeQL (requires GitHub token)
export GITHUB_TOKEN=”your-token”
codeql resolve languages
Run raptor on a target repo
python raptor.py –target https://github.com/victim/app –mode full
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What it does: Raptor will (1) clone the repo, (2) run semgrep for known bad patterns, (3) use CodeQL to build a data flow graph, (4) invoke the LLM to generate exploit code, and (5) fuzz with AFL++ to verify crashability. Output includes a patch file.
Cloud hardening tip: When scanning cloud-hosted code (e.g., AWS CodeCommit), use IAM roles with least privilege. Raptor can accept `–aws-profile` to assume a temporary role for read‑only access.
- OpenAnt: Open‑Source LLM Vulnerability Discovery (Stage 1 & 2)
OpenAnt, from Knostic, is a two‑stage framework: Stage 1 detects potential vulnerabilities using a lightweight LLM; Stage 2 attacks them with a stronger model to confirm exploitability.
Step‑by‑step:
bash
Clone OpenAnt
git clone https://github.com/knostic/openant.git
cd openant
Install (requires Python 3.10+)
pip install -r requirements.txt
Configure your LLM endpoints in config.json (supports OpenAI, Anthropic, local)
Run Stage 1 (detection)
python openant.py –stage 1 –target /path/to/code
Run Stage 2 (attack simulation) on the findings
python openant.py –stage 2 –findings findings.json
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Output: `verified_vulns.json` contains only issues that survived the attack stage. OpenAnt is ideal for CI/CD pipelines—integrate via `openant –stage 1 –target $CI_PROJECT_DIR` and fail the build if high‑severity verified vulnerabilities are found.
Windows note: Use WSL2 for OpenAnt due to its reliance on Linux fuzzing tools. Run wsl --install -d Ubuntu, then follow Linux instructions inside the WSL environment.
6. OSINT and GitHub Reconnaissance with AI
Raptor includes an OSINT module that scans GitHub for exposed secrets, vulnerable code patterns, and misconfigured workflows. This is invaluable for external attack surface management.
Command to run OSINT only:
bash
python raptor.py –mode osint –github-org target_org –output osint_report.json
[/bash]
Manual GitHub API + LLM analysis (Linux/Windows with curl and jq):
bash
Search for exposed API keys
curl -H “Authorization: token YOUR_GITHUB_TOKEN” \
“https://api.github.com/search/code?q=api_key+language:python” | jq ‘.items[] | .html_url’
Pipe results into for triage
echo “Analyze these URLs for false positives: $(cat urls.txt)” | -p “List only real secrets”
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Windows PowerShell equivalent:
bash
$token = “YOUR_GITHUB_TOKEN”
$headers = @{ Authorization = “token $token” }
$response = Invoke-RestMethod -Uri “https://api.github.com/search/code?q=secret+key” -Headers $headers
$response.items.html_url | Out-File urls.txt
[/bash]
API security best practice: Always use fine‑grained personal access tokens with repo scope only. Never paste tokens into LLM prompts—use environment variables or secret managers.
7. Responsible Disclosure and Validation Before Reporting
The post emphasizes: “As you consider submitting a vulnerability to coordinated disclosure, make absolutely sure it’s real and exploitable.” LLMs can hallucinate vulnerabilities. Always validate with manual testing or additional tooling.
Validation workflow:
bash
1. Replicate the crash using the agent’s PoC
python poc.py –target vulnerable_app
- Run a second opinion with a different LLM (e.g., GPT-4 vs )
openant –stage 2 –model gpt-4 –findings candidate.json -
Use traditional tools to confirm (e.g., Metasploit module or custom exploit)
msfconsole -q -x “use exploit/multi/http/my_vuln; set RHOSTS target; run”
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Linux command to check for false positives in C code (buffer overflow):
bash
gcc -fno-stack-protector -z execstack -o test test.c
./test $(python -c ‘print(“A”500)’) If no crash, likely false positive
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Windows (using Immunity Debugger or WinDbg): Attach to the process and feed the generated payload—watch for access violations.
Only after these steps should you report through platforms like HackerOne or direct vendor channels. This reduces the burden on open‑source maintainers and builds your reputation as a reliable researcher.
What Undercode Say:
- Key Takeaway 1: Agentic AI does not replace human intuition—it amplifies it. The most effective security professionals will become “AI co-pilots,” guiding LLMs with creative prompts and validating outputs.
- Key Takeaway 2: Open-source frameworks like raptor and OpenAnt lower the barrier to zero‑day discovery, but responsible disclosure remains a human duty—never trust a vulnerability report without manual verification.
- Analysis: The shift from “search engine partner” to “agentic co‑worker” mirrors the evolution from assembly to high‑level languages. Over the next 18 months, we’ll see AI agents integrated into every phase of the vulnerability management lifecycle—from reconnaissance to patching. However, the adversarial creativity that defines cybersecurity won’t disappear; instead, the battleground will move to prompt injection, model poisoning, and defending the agents themselves. Professionals who learn to orchestrate these tools today will become the architects of tomorrow’s security operations. Don’t panic, but don’t procrastinate—install Code or Cursor this week, run the Carlini loop on your own code, and experience the future firsthand.
Prediction:
Within two years, AI-driven vulnerability discovery will become a standard certification objective (e.g., OSCP+AI), and bug bounty platforms will split rewards between human researchers and their agentic tools. Entry‑level security roles focused on manual code review will decline, while “AI Security Engineer” positions—requiring skills in prompt engineering, agent orchestration, and LLM defense—will grow by over 300%. The democratization of zero‑day hunting will force vendors to adopt AI‑powered defensive agents in parallel, creating an arms race where speed of integration determines survival. Organizations that fail to train their security teams on agentic AI will face unmanageable technical debt and exposure to automated attacks from both script kiddies and nation‑states. The grass is still there—but now it’s being analyzed by a raptor.
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