Enhancing Security Testing with AI#

Artificial Intelligence, specifically Large Language Models (LLMs), is revolutionizing cybersecurity. It's not just about generating phishing emails; it's about augmenting the capabilities of penetration testers.

Use Cases for LLMs in Pentesting#

1. Code Analysis#

Feed a snippet of code to an LLM and ask for vulnerability analysis.

"Analyze this PHP function for SQL injection vulnerabilities and suggest a fix."

2. Payload Generation#

AI can generate context-specific payloads that bypass filters.

"Generate a Polyglot XSS payload that works in a JSON context and bypasses a regex checking for 'script'."

3. Report Writing#

Automating the boring stuff. AI can turn your technical findings into a polished executive summary.

Tools Integrating AI#

• Burp Suite: Extensions like "GPT-4 for Burp" are emerging.
• OWASP Noir: Recent updates include AI-driven attack surface analysis.
• Custom Scripts: Hackers are writing Python scripts that pipe tool output to OpenAI's API for real-time analysis.

The Risks#

• Hallucinations: AI can confidently invent vulnerabilities that don't exist.
• Data Privacy: Be careful not to send sensitive client code to public LLM APIs.

Conclusion#

AI won't replace pentesters, but pentesters who use AI will replace those who don't.