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Two recent developments highlight the growing role of AI in vulnerability disclosure:
1. OpenAI’s Outbound Coordinated Vulnerability Disclosure Policy (Link)
2. HackerOne’s inclusion of AI-powered “hackbots” in responsible disclosure programs (Link)
AI is no longer just a target for vulnerabilities—it’s now an active discoverer and reporter of security flaws. This shift will lead to a surge in disclosed vulnerabilities, raising critical questions about coordination, signal-to-noise ratios, and accountability.
You Should Know: Practical AI Vulnerability Management
1. Detecting AI-Generated False Positives
AI can hallucinate vulnerabilities. Use these commands to verify findings:
Linux (Bash) – Log & Code Analysis
Check for false CVE reports in logs
grep -i "CVE-" /var/log/syslog | awk '{print $NF}' | sort | uniq -c
Cross-reference with NVD database
curl -s "https://services.nvd.nist.gov/rest/json/cves/2.0?cveId=CVE-2023-1234" | jq '.vulnerabilities[].cve.descriptions[] | select(.lang=="en").value'
Windows (PowerShell) – Validate Exploits
Check if a reported DLL hijack exists
Get-ChildItem -Path "C:\Windows\System32\" -Filter ".dll" | Where-Object { $_.Name -eq "reported_malicious.dll" }
Test open ports (AI may misreport)
Test-NetConnection -ComputerName localhost -Port 445
2. Automating Triage with AI Assistants
Use YARA rules to filter AI-generated noise:
rule AI_Generated_Vuln_Report {
meta:
description = "Detect low-effort AI vulnerability reports"
strings:
$ai_phrases = /"likely vulnerable"|"potential exploit"|"further analysis needed"/ nocase
condition:
$ai_phrases and filesize < 50KB
}
3. AI-Enhanced Penetration Testing
Run Burp Suite with AI Plugins:
java -jar burpsuite.jar --use-ai-scan --config=ai_scan_config.json
4. Monitoring AI-Generated Attacks
Detect AI-driven brute-force attempts with Fail2Ban:
/etc/fail2ban/jail.local [ai-bruteforce] enabled = true filter = sshd maxretry = 3 findtime = 1h bantime = 24h
What Undercode Say
AI is reshaping cybersecurity, but human oversight remains critical. Key takeaways:
– Verify AI reports before acting.
– Automate triage with rules and scripts.
– Train teams to spot AI-generated false positives.
– Use AI defensively (e.g., filtering noise).
Prediction
By 2026, 50% of bug bounty reports will be AI-generated, forcing platforms to adopt stricter validation.
Expected Output:
- Verified AI vulnerability reports
- Reduced false positives via automated checks
- Faster response times with AI-assisted triage
IT/Security Reporter URL:
Reported By: Michiel3 Two – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
