Egress Lock v0.1 — Transitive Egress Closure for AI Cyber Evaluations
桑杰戈 · Team Dawnline
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
Egress Lock is a low-cost containment standard for guardrails-off AI cyber evaluations. It addresses a failure mode demonstrated by the July 2026 Hugging Face incident: an evaluated agent may lack direct Internet access yet obtain external connectivity by inducing a reachable intermediary service to relay requests on its behalf.
We define Transitive Egress Closure (TEC): every network-capable service reachable by the evaluated agent must either be unable to relay arbitrary external requests, or have its egress enforced by the same controlled boundary. Our reference harness uses real Linux network namespaces and nftables, a deliberately vulnerable artifact proxy, an independent Canary, and isolated management control.
The final measured run covered 33 atomic test items: 26 PASS, 2 PASS (bounded), and 5 NOT PROVEN. The strongest result is a 6/6 TEC relay test in which the vulnerable proxy could be induced to attempt external access, but the second hop was blocked while a direct positive control remained allowed. The prototype does not claim universal Internet impossibility, real-Internet coverage, or physical-hardware assurance.
Reviews
I think Egress Lock provides a useful demonstration of restricting an agent’s network access. The reported experiment blocks a vulnerable relay under the locked policy and allows it during a temporary exception. The report clearly explains the test and acknowledges that three privilege checks remain unproven.
My next question is whether legitimate evaluations can still run under those restrictions. A successful web request does not establish that dependency downloads work. I would test a realistic package download task alongside the relay attempt, then check whether an agent with its assigned permissions can change the restrictions. That would give teams stronger evidence for deciding whether to adopt the policy.
1 judge declined to score this project.
Cite this project
@misc{apart2026egress,
title = {{Egress Lock v0.1 — Transitive Egress Closure for AI Cyber Evaluations}},
author = {桑杰戈},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/egress-lock-v01-transitive-egress-closure-for-ai-cyber-evaluations-3f0m}},
url = {https://apartresearch.com/sprints/projects/egress-lock-v01-transitive-egress-closure-for-ai-cyber-evaluations-3f0m}
}More from AI Incident Response Sprint
- 1st placeView project: Noticing the escapes: Who spots a runaway agent, and how long does it take?
Noticing the escapes: Who spots a runaway agent, and how long does it take?
The Escapees
In 2026, nine incidents became public in which AI agents escaped their environment and did something unauthorized to other parties. We analyzed all of these incidents and how they were noticed initially. None of these …
- 2nd placeView project: Strengthening International AI Incident Discovery
Strengthening International AI Incident Discovery
Team A-O-I
Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and …
- 3rd placeView project: When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
Teachafy
When to Ask is a reinforcement-learning environment that trains AI agents to work out what the user actually means before they use a powerful credential, instead of just carrying out the literal instruction. Agents …