Real findings from authorized penetration testing engagements. These case studies show how individual weaknesses chain together into critical attack paths — the kind automated scanners never find.
During an authorized penetration test of a leading large language model (LLM) platform, we identified critical weaknesses in the AI safety mechanisms designed to prevent harmful outputs. Automated security tools flagged zero of these issues.
All findings are derived from explicitly authorized penetration testing engagements with defined scope and rules of engagement.
We prioritize real-world attack chains rather than isolated vulnerabilities or automated scanner output. Every finding is manually verified.
Each case study highlights root causes, impact, and remediation guidance relevant to engineering teams.
Every organization has attack paths they don't know about. Let us find yours before an attacker does.