BackDeep Learning Frameworks
Deep Learning Frameworks
Risk Domain
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.
"LLMs are implemented based on deep learning frameworks. Notably, various vulnerabilities in these frameworks have been disclosed in recent years. As reported in the past five years, three of the most common types of vulnerabilities are buffer overflow attacks, memory corruption, and input validation issues."(p. 9)
Entity— Who or what caused the harm
Intent— Whether the harm was intentional or accidental
Timing— Whether the risk is pre- or post-deployment
Part of Software Security Issues
Other risks from Cui et al. (2024) (49)
Harmful Content
1.2 Exposure to toxic contentAI systemUnintentionalPost-deployment
Harmful Content > Bias
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalOther
Harmful Content > Toxicity
1.2 Exposure to toxic contentAI systemUnintentionalPost-deployment
Harmful Content > Privacy Leakage
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationAI systemUnintentionalPost-deployment
Untruthful Content
3.1 False or misleading informationAI systemUnintentionalPost-deployment
Untruthful Content > Factuality Errors
3.1 False or misleading informationAI systemUnintentionalPost-deployment