AI tools are making it easier to write security reports, but they are also creating new problems for organisations that depend on reliable vulnerability disclosures. A new report covered by BleepingComputer describes how AI-generated submissions are overwhelming some bug bounty programmes and making it harder for beginners to learn effective security research practices.

For European businesses, this is more than a community issue for ethical hackers. It has practical implications for how companies handle external vulnerability reports, assess the quality of findings, and train employees or contractors who work on security testing.

What happened

According to BleepingComputer, cybersecurity training platform TryHackMe said that some bug bounty programmes are seeing a rise in AI-generated vulnerability reports. The concern is not simply that AI is being used, but that low-quality or poorly validated submissions can create additional workload for security teams and reduce the signal-to-noise ratio in disclosure programmes.

The report argues that beginners should focus on building practical skills, learning how systems actually work, and producing findings that are reproducible and verifiable. It also highlights the importance of using AI responsibly rather than relying on it to generate claims that cannot be properly tested.

Why it matters for European businesses

Many companies, including SMEs, rely on a mix of internal IT staff, managed security providers, external researchers, and software vendors to identify vulnerabilities. If AI-generated reports increase the volume of weak or inaccurate submissions, security teams may need more time to triage incoming findings before they can identify genuine risks.

This can affect businesses in several ways:

• More triage workload: Internal teams or service providers may spend additional time reviewing reports that look convincing but lack evidence.
• Slower remediation: Real vulnerabilities may take longer to verify and fix when teams are dealing with more low-quality submissions.
• Training quality risks: Companies building internal security capabilities may find that staff over-rely on AI tools instead of developing core testing and validation skills.
• Procurement and vendor oversight: Businesses using penetration testing or bug bounty services may need clearer expectations around evidence, reproducibility, and report quality.

The issue is also relevant as more organisations introduce AI into software development, testing, and security operations. AI can improve efficiency, but security workflows still depend on human validation, technical understanding, and clear documentation.

Who may be affected

• SMEs with public websites, apps, or customer portals that receive unsolicited vulnerability reports or run formal disclosure processes.
• IT and security teams responsible for validating findings from external researchers, consultants, or automated tools.
• Digital agencies and development providers that manage websites and applications for clients and may be asked to review third-party security claims.
• Companies adopting AI in development or security operations and trying to define acceptable use of AI-assisted testing and reporting.
• Organisations in regulated sectors where weak handling of vulnerabilities can create broader governance and compliance concerns.

What companies should consider

European businesses do not need to avoid AI in cybersecurity, but they should be careful about how it is used in testing and reporting. Practical steps may include:

• Require reproducible evidence: Ask for clear proof-of-concept steps, affected assets, impact explanation, and conditions needed to reproduce the issue.
• Set disclosure standards: If your company accepts vulnerability reports, define minimum requirements for submissions and explain what evidence is needed.
• Review AI use in security workflows: Establish internal guidance on where AI can support research, documentation, or analysis, and where human validation is mandatory.
• Train for fundamentals: Encourage staff and junior testers to understand web security, authentication, input handling, misconfiguration, and application logic rather than depending only on AI-generated output.
• Vet external providers carefully: When buying penetration testing or security assessment services, ask how findings are validated and how AI-assisted work is reviewed.
• Improve triage processes: Use structured intake, severity classification, and escalation paths so genuine vulnerabilities can be identified faster.

The core message from the report is practical: AI can assist cybersecurity work, but credible vulnerability discovery still depends on technical accuracy and evidence. For businesses, the priority is to make sure security decisions are based on validated findings rather than persuasive-looking automated reports.