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Programming Language

Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems

Cui et al. (2024)

SourceDOI
Sub-category
Risk Domain
2Privacy & Security
2.2AI system security vulnerabilities and attacks

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.

"Most LLMs are developed using the Python language, whereas the vulnerabilities of Python interpreters pose threats to the developed models"(p. 9)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Part of Software Security Issues

Other risks from Cui et al. (2024) (49)

Harmful Content

1.2 Exposure to toxic content
AI systemUnintentionalPost-deployment

Harmful Content > Bias

1.1 Unfair discrimination and misrepresentation
AI systemUnintentionalOther

Harmful Content > Toxicity

1.2 Exposure to toxic content
AI systemUnintentionalPost-deployment

Harmful Content > Privacy Leakage

2.1 Compromise of privacy by leaking or correctly inferring sensitive information
AI systemUnintentionalPost-deployment

Untruthful Content

3.1 False or misleading information
AI systemUnintentionalPost-deployment

Untruthful Content > Factuality Errors

3.1 False or misleading information
AI systemUnintentionalPost-deployment
View all 49 risks from this paper →