CWE

Troubleshooting AI Bug Hunting: Seven Critical Mistakes That Destroy Report Validity

Two Russian researchers shared detailed lessons from six months of running AI agents on domestic bug bounty platforms. The presentation, originally delivered at OFFZONE, covers hallucinations, lack of validation, threat model misunderstandings, severity inflation, bloated reports, high token costs, and scope violations. Vladislav Bek and Daniil Shushakov describe practical fixes including tool-enabled agents, dual-agent verification, strict templates, and cascaded model architectures. They emphasize that AI must execute real commands in isolated environments rather than fabricate results. The speakers also address scaling on private events, handling model refusals, and the continued necessity of human final validation. Their checklist stresses business context, proven impact, and staying within program boundaries to produce accepted, high-value reports.

From Hundreds of Alerts to Proven Vulnerabilities: INFERA AI.SafeCode Unifies Seven Scanners into a Single DevSecOps Pipeline

INFERA AI.SafeCode integrates seven distinct security scanners into one continuous analysis platform that automatically validates findings instead of flooding teams with unconfirmed alerts. The solution combines SAST, SCA, Secrets detection, DAST, AI-driven Pentest agents, Code Fuzzing, and API Fuzzing to deliver proof-of-exploit evidence for high-risk issues. By cross-validating results across engines, the platform reduces false positives and provides developers with actionable tasks that include reproduction steps, stack traces, and one-click AutoFix recommendations directly inside IDEs and Git workflows. Special attention is given to AI-generated code from tools such as GitHub Copilot, Cursor, and Claude, ensuring that rapid development does not introduce unvetted vulnerabilities. The system also maps full attack surfaces, tracks reachability from entry points to vulnerable sinks, and supports compliance requirements including FSTEC orders for critical information infrastructure. MLSecOps capabilities extend coverage to machine-learning pipelines, model configurations, and inference APIs. Overall, INFERA shifts AppSec from reactive alert triage to measurable risk management with clear MTTR metrics and SLA tracking.