Fine-tuning related (Poisoning models during instruction tuning)
Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.
"AI models can be poisoned during instruction tuning when models are tuned using pairs of instructions and desired outputs. Poisoning in instruction tuning can be achieved with a lower number of compromised samples, as instruction tuning requires a relatively small number of samples for fine-tuning [155, 211]. Anonymous crowdsourcing efforts may be employed in collecting instruction tuning datasets and can further contribute to poisoning attacks [187]. These attacks might be harder to detect than traditional data poisoning attacks."(p. 14)
Other risks from Gipiškis et al. (2024) (144)
Direct Harm Domains (content safety harms)
1.2 Exposure to toxic contentDirect Harm Domains (content safety harms) > Violence and extremism
1.2 Exposure to toxic contentDirect Harm Domains (content safety harms) > Hate and toxicity
1.2 Exposure to toxic contentDirect Harm Domains (content safety harms) > Sexual content
1.2 Exposure to toxic contentDirect Harm Domains (content safety harms) > Child harm
1.2 Exposure to toxic contentDirect Harm Domains (content safety harms) > Self-harm
1.2 Exposure to toxic content