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Pentesting

4 GoKawiil briefs on this topic

Proton CEO scraps planned AI-usage 're-interviews' after employee backlash report

Proton CEO Andy Yen called off nearly company-wide 're-interviews' that would have required staff to justify their AI usage, a plan that employees feared could lead to firings. The reversal, detailed in a two-hour all-hands meeting, follows a 404 Media investigation showing Yen aggressively pushing AI adoption at Proton, including dismissing employee security concerns about rushing AI-written code into production.

South Korean police arrest seven over AI-driven breach of 90+ organizations

Seoul Metropolitan Police say a seven-member gang, led by a former penetration tester, breached more than 90 South Korean organizations starting in September 2025 and stole 8.2 million records. The group used the ARTEX AI pentesting framework in most attacks and demanded ransom payments of 360 million won ($270,000) in 38 cases to avoid leaking stolen data. The leader was arrested on October 15 and referred to prosecutors on October 23; the other six suspects remain under investigation but not detained.

Intruder says rigid methodology hampers AI pentesting agents

Intruder, the company behind an AI-driven pentesting tool, reports that early attempts to assign AI agents strict, narrowly defined vulnerability-hunting tasks caused them to miss issues outside their assigned scope or misclassify findings. Giving agents no structure at all also failed, as unguided agents couldn't properly manage large applications despite occasionally finding interesting issues. The company says it settled on a middle ground: broad roles with freedom to explore, paired with guardrails to prevent damage.

Picus Security details gaps in AI-driven penetration testing tools

A Picus Security engineer outlines how agentic pentesting—where AI agents autonomously run reconnaissance, exploitation and reporting steps—has emerged as a faster alternative to scheduled automated pentests, citing Picus's own Swarm product as an example. The piece argues this approach can uncover exploit chains humans might miss, but notes it carries tradeoffs, including heavy token/compute usage, and identifies blind spots that need to be addressed.