HFEPX · Eval paper review

Defending Against Dormant Poisoning Attacks Across Language and Multimodal Agents

Wonjun Lee, Vaidehi Patil, Zaid Khan, Yue Zhang +3 more

Published

Oct 17, 2026

Citations

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

15% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 17, 2026

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Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Abstract-only analysis, low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.
The abstract does not clearly name benchmarks or metrics.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal

Not explicit

Not explicit in abstract metadata

Evaluation signal

Weak or implicit

Validate from full paper

Usefulness for eval research

0/100

Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

Recent work shows that attackers can implant dormant malicious behaviors that remain hidden at release but emerge after benign fine-tuning. As foundation models are used as agents, whether dormant poisoning extends to harmful agent actions and multimodal settings remains unclear. We show two agentic risks: (1) harmful behaviors activated by benign fine-tuning can extend to unseen actions; (2) multiple harmful behaviors can coexist and be jointly activated by benign downstream fine-tuning. Existing defenses often rely on external safe responses, which are hard to obtain for agent actions, or a trusted clean reference model. We exploit a key property of dormant poisoning: the attacker must preserve safe behavior at release to conceal poisoning, and this behavior can serve as a safety reference. Leveraging this property, we automatically construct a Safety Buffer pairing harmful inputs with the released model's responses and benign versions generated by the same model to remove harmful intent. The Safety Buffer can replace external safe responses in existing defenses, but these defenses do not distinguish updates that weaken safety from those that preserve it, often increasing over-refusal or reducing utility. To address this limitation, we introduce LookAhead Defense, which previews benign fine-tuning updates using the Safety Buffer and penalizes only updates predicted to weaken safe behavior on harmful inputs or increase the likelihood of refusals on benign versions in the Safety Buffer. Across language models, language agents, and multimodal agents, LookAhead Defense reduces harmful behavior in agents from up to 93.9% to at most 14.2% and harmful responses in language models from up to 92.5% to at most 2.0% versus vanilla fine-tuning, while maintaining competitive utility and low over-refusal. It remains effective against other poisoning methods.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.