arXiv:2509.06896v2 Announce Type: replace Abstract: Targeted data poisoning attacks manipulate model predictions on specific test samples by injecting malicious data into training. Yet existing evaluations report average attack success rates over randomly selected targets, obscuring true worst-case effectiveness. We argue that the right evaluation focuses on the hardest samples to poison. The same reasoning applies to defense: since targeted attacks leave no footprint at the distribution level, defenders should proactively identify the most vulnerable samples and apply targeted countermeasures
The increasing deployment of AI models in critical applications makes understanding their vulnerabilities, like data poisoning, more urgent.
This research refines our understanding of AI model robustness, highlighting worst-case vulnerabilities rather than average attack success, which is crucial for secure AI development and deployment.
The focus for evaluating AI security shifts from average attack success rates to identifying and protecting the most vulnerable samples in training data, impacting defense strategies.
- · AI Security Researchers
- · Organizations deploying AI in critical sectors
- · Ethical AI developers
- · Malicious actors relying on average attack metrics
- · AI systems with undifferentiated defense mechanisms
AI model developers will need to implement more sophisticated, targeted defense mechanisms against poisoning attacks.
Increased investment in data provenance and integrity solutions will become necessary across the AI supply chain.
Regulatory bodies may begin to mandate specific robustness testing against worst-case data poisoning scenarios for deployed AI.
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Read at arXiv cs.LG