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Tencent Zhuque Lab benchmark lifts agent harm rates to 95% with one poisoned handoff
Tencent Zhuque Lab's RogueHandoff-20 benchmark finds one poisoned handoff lifts receiving agents' harm rates from 0-5% to 40-95% across four routes. Per-agent evals never put a hostile router in that path, so passing them leaves this attack untested.
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What happened
- The attack runs through a modified router built on Qwen-27B that sits between the sending and receiving agents and injects an unsafe trajectory into the transition.
- By the time the receiving agent acts, the request in front of it can look entirely benign when read on its own.
- The benchmark's 20 executable scenarios range from incident response to model shutdown.
- Zhuque Lab contributed the benchmark through a GitHub pull request to Tencent's AI-Infra-Guard project.
Compiled by The EngineerSomething wrong?How this is made
Why it matters
- exposure Anyone who can modify the router between agents can steer what the receiver does, so the router becomes an attack surface on the same footing as the agents.
- decision The harmful rate moves by 11 of 20 scenarios between routes, so picking a handoff architecture is a safety decision in its own right.
- cost Covering this risk means building a hostile-router test for each handoff route a pipeline uses, on top of the existing per-agent eval suite.
Every RogueHandoff-20 number assumes the attacker has already modified the router that sits between the agents [5]. That is a strong threat model. I think it is a fair one for any pipeline whose router is itself a model.
The dev.to write-up that carried the results separates this attack from prompt injection [9]. In prompt injection the payload sits inside the message, and content filters ask whether the request is harmful [9]. In handoff injection no single inspected message holds the full attack. The write-up puts the defender's question as "how did this task arrive?" [9].
The false-comfort argument needs one qualification. The write-up opens by saying every agent passes when tested alone [12]. The baseline behind that pass is the harm rate on normal tasks [2]. So the benchmark compares benign work with poisoned handoffs. It does not show an agent passing a harmful-prompt eval on its own and then failing inside a pipeline. The write-up's narrower claim holds up on the evidence: a team that has only run per-agent safety evaluations has an unverified posture for this risk [11].
Twenty scenarios is a small set [1]. Each scenario moves a route's rate by 5 points [1]. The worst route's 95% is roughly 19 of 20 cases [4]. At one run per scenario, the best route's 40% is 8 of 20 [2]. The write-up calls the range a "four-to-one spread" [10]. Dividing 95 by 40 gives about 2.4 [3]; four is the number of routes. The gap between best and worst is still 55 points, or 11 of 20 scenarios [4].
For the 95% to transfer, a pipeline's handoff has to resemble the worst route, its receiver has to behave like the benchmark's, and an attacker has to be able to rewrite what passes between agents. The write-up does not name the four routes or the receiving models. Its figures also come second-hand, through explainx.ai coverage of the pull request [8].
The craft here is that the attack is a component. A table of harm rates cannot be rerun against your own stack. A modified router [5] and a set of executable scenarios [7] can be, against a different receiver and a different handoff design.
What to watch
- Whether the AI-Infra-Guard pull request publishes the names of the four handoff routes and the receiving models, so the 40-95% range can be mapped to real designs.
- Results with defenses applied at the transition, showing whether the worst route's roughly 19-of-20 rate comes down.
- Replication with more scenarios or repeated runs, which would tighten the 5-point resolution of each route's rate.