XBOW, an autonomous penetration tester, has reached the top spot on HackerOne

Quality and Validity of XBOW’s Findings

• XBOW claims all reported vulnerabilities were real and accompanied by executable proof-of-vulnerability; some commenters ask directly if that implies a 0% false-positive rate.
• The article mentions automated “validators” (LLM- or script-based) to confirm each finding (e.g., headless browser to verify XSS), but people note it doesn’t quantify how many candidate bugs were discarded before the ~1,060 reports.
• Success rates differ sharply by target (e.g., very high validity for some programs, very low for others), which commenters attribute partly to varying program policies (third-party issues, excluded vuln classes, “never mark invalid,” etc.).

AI Slop, Noise, and Triage Burden

• Multiple maintainers describe AI-generated “slop” reports as demoralizing (e.g., placeholder API keys flagged as leaks) and expect AI to massively industrialize low-quality submissions.
• Others note bug bounty programs already receive an overwhelming volume of terrible human submissions; platforms like HackerOne exist partly to shield companies from this.
• Concern: XBOW’s ~1,060 submissions consume triage capacity; stats from its own breakdown show many duplicates, “informative,” or “not applicable” reports, which still cost reviewer time.

Automation vs. Human Involvement

• Some see XBOW as a strong, pragmatic AI use case because working exploits are hard evidence and reduce hallucination risk.
• Others stress that humans still design the system, prompts, tools, and validators, and review reports before submission; calling it “fully autonomous” is seen as marketing overreach.
• There’s skepticism that such a system could run unattended for months and continue to produce high-value bugs without ongoing human tuning.

Bug Bounty Ecosystem and Ethics

• Several participants describe bug bounties as economically skewed: many low-paying programs, slow payouts, and companies allegedly using them for near-free security work.
• Some argue many companies shouldn’t run bounties at all; they’d be better off hiring security firms.
• Ethical concerns arise over using automated tools where program rules forbid automation; others counter that if a human can reproduce the bug, the discovery method shouldn’t matter.

Broader Impact on Security and Talent

• Many view AI-assisted pentesting as ideal for clearing “low-hanging fruit,” especially in legacy code, and freeing experts for more creative work.
• Others worry about triage scalability, the flood of mediocre AI reports hiding real issues, and long-term effects on training and opportunities for junior security researchers.