BadSkill poisons embedded models in agent skills to achieve up to 99.5% attack success rate on triggered tasks with only 3% poison rate while preserving normal behavior on non-trigger inputs.
A study of backdoors in instruction fine-tuned language models
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.
citing papers explorer
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BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning
BadSkill poisons embedded models in agent skills to achieve up to 99.5% attack success rate on triggered tasks with only 3% poison rate while preserving normal behavior on non-trigger inputs.
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Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
Succinct Model Difference Proofs certify that a neural-network update stays inside a policy-defined drift class using zero-knowledge proofs whose cost depends only on the drift structure.
- Backdooring Masked Diffusion Language Models