MIT AI Risk Repository · Risk Sub-Category · 62.18.03

Adversarial attacks targeting explainable AI techniques

Category: Model Evaluations (Interpretability/Explainability)

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Description

"Adversarial attacks can affect not only the model’s output but also its corresponding explanation. Current adversarial optimization techniques can intro- duce imperceptible noise to the input image, so that the model’s output does not change but the corresponding explanation is arbitrarily manipulated [61]. Such manipulations are harder to notice, as they are less commonly known compared to standard adversarial attacks targeting the model’s output."

From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 2. Privacy & Security
Subdomain: 2.2 AI system security vulnerabilities and attacks
Causal entity: Human
Intent: Intentional
Timing: Other

Subdomain definition: Vulnerabilities in AI systems, software development toolchains, and hardware that can be exploited, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.

Real-world incidents in this subdomain

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How other frameworks describe this risk

Privacy loss
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Exploiting Limited Generalization of Safety Finetuning
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Jailbreaks and Prompt Injections Threaten Security of LLMs
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
“Model Psychology” Attacks
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Attacking LLMs via Additional Modalities a
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Vulnerability to Poisoning and Backdoors
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Model Attacks
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Hardware Vulnerabilities
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Other entries from Gipiškis2024

Dimension - Intent Risk Category
Intentional Risk Sub-Category
Unintentional Risk Sub-Category
Partially intentional Risk Sub-Category
Dimension - Entity Risk Category
Human Risk Sub-Category
AI Risk Sub-Category
Combination of humans and AI Risk Sub-Category
Dimension - Failure dynamics Risk Category
Isolated (non-normal) failures Risk Sub-Category
System (normal) failures Risk Sub-Category
System (normal) failures Additional evidence