MIT AI Risk Repository · Risk Sub-Category · 64.04.07

Poisoning

Category: Misuse tactics to compromise GenAI systems (Model integrity)

Open the MIT Risk Navigator All entries from Marchal2024

Description

"Data Poisoning involves deliberately corrupting a model’s training dataset to introduce vulnerabilities, derail its learning process, or cause it to make incorrect predictions (Carlini et al., 2023). For example, the tool Nightshade is a data poisoning tool, which allows artists to add invisible changes to the pixels in their art before uploading online, to break any models that use it for training.9 Such attacks exploit the fact that most GenAI models are trained on publicly available datasets like images and videos scraped from the web, which malicious actors can easily compromise."

From Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data (Marchal2024), 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: Pre-deployment

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 Marchal2024

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Appropriated Likeness Additional evidence
Sockpuppeting Risk Sub-Category · 4.1
Sockpuppeting Additional evidence
Non-consensual intimate imagery (NCII) Risk Sub-Category · 4.3
Non-consensual intimate imagery (NCII) Additional evidence
Child sexual abuse material (CSAM) Risk Sub-Category · 4.3
Child sexual abuse material (CSAM) Additional evidence
Misuse tactics that exploit GenAI capabilities (Realistic depictions of non-humans) Risk Category · 4.3