OF COURSE IT ESCAPES: HOW ZEROQUARRY CONTAINS SECURITY AGENTS

Summary from the TLDR InfoSec newsletter

ZeroQuarry's AI pentesting agents use four layers that never depend on the model's cooperation: outbound-only workers with no listener, a locked-down container, tools that enforce scope, SSRF, and rate limits in code, and rules appended to every task by code. The vendor argues that this year's sandbox escapes, including the OpenAI and Hugging Face incident involving at least 1,200 agents, stemmed from harnesses built to maximize capability, and it self-reports zero escapes across thousands of scans. Teams building agent harnesses should give agents a sanctioned, metered tool for each risky action, such as a one-way out-of-band callback collector, so agents don't improvise their own.