arXiv:2605.26595v1 Announce Type: cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on fixed trigger phrases that defenses such as outlier detection, clean-data regularization, or online monitoring can neutralize. In this paper, we propose a data poisoning method that teaches an LLM an information hiding scheme reliably and stealthily through semantic associations between shared knowledge such as facts or concepts and attacker-chosen phrases. The induced hiding scheme can encode an
The proliferation of LLMs and their fine-tuning on less curated datasets creates new attack surfaces, making covert poisoning methods increasingly relevant.
This research reveals a sophisticated new vector for adversarial control over LLMs, undermining trust and potentially enabling widespread disinformation or malicious instruction embedding.
Adversaries can now embed covert control mechanisms in LLMs via data poisoning, circumventing existing defenses that rely on detecting explicit trigger phrases.
- · Threat intelligence firms
- · Cybersecurity researchers
- · AI red teamers
- · LLM developers
- · Organizations relying on unverified LLMs
- · Users of poisoned LLMs
- · Data providers
Increased scrutiny and investment in supply chain security for LLM training data will become paramount.
New regulatory frameworks may emerge to mandate transparency and auditability of LLM training datasets and methodologies.
A 'trust deficit' in public-facing or critical LLM applications could grow, potentially slowing AI adoption in sensitive sectors.
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Read at arXiv cs.LG