arXiv:2602.04899v2 Announce Type: replace-cross Abstract: We present a data poisoning attack -- Phantom Transfer -- with the property that, even if you know precisely how the poison was placed into an otherwise benign dataset, you cannot filter it out. We achieve this by modifying subliminal learning to work in real-world contexts and demonstrate that the attack works regardless of which model produced the data, which model is trained on the data or what the attack target is. Furthermore, the attack survives 11 tested data-level defences, including one where every sample is paraphrased by anot
The proliferation of AI models and the increasing reliance on large datasets make data poisoning an increasingly attractive and effective attack vector, necessitating robust defenses.
This research reveals a critical vulnerability in AI systems, demonstrating that sophisticated data poisoning attacks can bypass current data-level defenses, posing significant risks to AI integrity and security.
Existing data hygiene practices and filtering mechanisms are now shown to be insufficient against advanced data poisoning techniques, requiring a re-evaluation of AI security strategies.
- · AI security researchers
- · AI defense solution providers
- · Organizations prioritizing AI explainability
- · Developers of data-level AI defenses
- · Organizations relying on unchecked data for AI training
- · AI systems vulnerable to data manipulation
Increased focus on robust AI model auditing and post-training defense mechanisms.
Potential for 'AI data wars' where adversaries intentionally poison training datasets to degrade competitors' AI capabilities.
Erosion of public trust in AI systems due to undetectable manipulation, leading to calls for stricter AI regulation and certification.
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Read at arXiv cs.AI