Awesome

Awesome-ML-Security

A curated list of awesome machine learning security references, guidance, tools, and more.

Table of Contents

• Awesome-ML-Security
  • Relevant work, standards, literature
    • CIA of the model
      • Confidentiality
      • Integrity
      • Availability
    • Degraded model performance
    • ML-Ops
    • AI’s effect on attacks/security elsewhere
      • Self-driving cars
      • LLM Alignment
  • Regulatory actions
    • US
    • EU
  • Safety standards
  • Taxonomies and frameworks
  • Security tools and techniques
    • API probing
    • Model backdoors
  • DeepFakes, disinformation, and abuse
  • Background information
  • Notable incidents
  • Notable harms

Relevant work, standards, literature

CIA of the model

Membership attacks, model inversion attacks, model extraction, adversarial perturbation, prompt injections, etc.

• Towards the Science of Security and Privacy in Machine Learning
• SoK: Machine Learning Governance
• Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What To Do About Them
• On the Impossible Safety of Large AI Models

Confidentiality

Reconstruction (model inversion; attribute inference; gradient and information leakage), theft of data, Membership inference and reidentification of data, Model extraction (model theft), property inference (leakage of dataset properties), etc.

• awesome-ml-privacy-attacks
• Privacy Side Channels in Machine Learning Systems
• Beyond Labeling Oracles: What does it mean to steal ML models?
• Text Embeddings Reveal (Almost) As Much As Text
• Language Model Inversion
• Extracting Training Data from ChatGPT
• Recovering the Pre-Fine-Tuning Weights of Generative Models

Integrity

Backdoors/neural trojans (same as for non-ML systems), adversarial evasion (perturbation of an input to evade a certain classification or output), data poisoning and ordering (providing malicious data or changing the order of the data flow into an ML model).

• A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Examples
• Poisoning Web-Scale Training Datasets is Practical
• Planting Undetectable Backdoors in Machine Learning Models
• Motivating the Rules of the Game for Adversarial Example Research
• On Evaluating Adversarial Robustness
• Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
• Universal and Transferable Adversarial Attacks on Aligned Language Models
• Manipulating SGD with Data Ordering Attacks
• Adversarial reprogramming - repurposing a model for a different task than its original intended purpose
• Model spinning attacks (meta backdoors) - forcing a model to produce output that adheres to a meta task (for ex. making a general LLM produce propaganda)
• LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?
• Securing LLM Systems Against Prompt Injection & Mitigating Stored Prompt Injection Attacks Against LLM Applications
  • Best Practices for Securing LLM-Enabled Applications
  • NVIDIA NeMo Guardrails: Security Guidelines

Availability

• Energy-latency attacks - denial of service for neural networks

Degraded model performance

• Trail of Bits's Audit of YOLOv7
• Robustness Testing of Autonomy Software
• Can robot navigation bugs be found in simulation? An exploratory study
• Bugs can optimize for bad behavior (OpenAI GPT-2)
• You Only Look Once Run time errors

ML-Ops

• Auditing the Ask Astro LLM Q&A app
• Exploiting ML models with pickle file attacks: Part 1 & Exploiting ML models with pickle file attacks: Part 2
• PCC: Bold step forward, not without flaws
• Trail of Bits's Audit of the Safetensors Library
• Facebook’s LLAMA being openly distributed via torrents
• Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
• DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection
• Weaponizing Machine Learning Models with Ransomware (and Machine Learning Threat Roundup)
• Bug Characterization in Machine Learning-based Systems
• LeftoverLocals: Listening to LLM responses through leaked GPU local memory
• Offensive ML Playbook

AI’s effect on attacks/security elsewhere

• How AI will affect cybersecurity: What we told the CFTC
• Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
• Examining Zero-Shot Vulnerability Repair with Large Language Models
• Do Users Write More Insecure Code with AI Assistants?
• Learned Systems Security
• Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection
• Data-Driven Offense from Infiltrate 2015
• Codex (and GPT-4) can’t beat humans on smart contract audits

Self-driving cars

• Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?

LLM Alignment

• When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning

Regulatory actions

US

• FTC: Keep your AI claims in check
• FAA - Unmanned Aircraft Vehicles
• NHTSA - Automated Vehicle safety
• AI Bill of Rights
• Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence

EU

• The Artificial Intelligence Act (proposed)

Other

• TIME Ideas: How AI Can Be Regulated Like Nuclear Energy
• Trail of Bits’s Response to OSTP National Priorities for AI RFI
• Trail of Bits’s Response to NTIA AI Accountability RFC

Safety standards

• Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems
• ISO/IEC 42001 — Artificial intelligence — Management system
• ISO/IEC 22989 — Artificial intelligence — Concepts and terminology
• ISO/IEC 38507 — Governance of IT — Governance implications of the use of artificial intelligence by organizations
• ISO/IEC 23894 — Artificial Intelligence — Guidance on Risk Management
• ANSI/UL 4600 Standard for Safety for the Evaluation of Autonomous Products — addresses fully autonomous systems that move such as self-driving cars, and other vehicles including lightweight unmanned aerial vehicles (UAVs). Includes safety case construction, risk analysis, design process, verification and validation, tool qualification, data integrity, human-machine interaction, metrics and conformance assessment.
• High-Level Expert Group on AI in European Commission — Ethics Guidelines for Trustworthy Artificial Intelligence

Taxonomies and frameworks

• NIST AI 100-2e2023
• MITRE ATLAS
• AI Incident Database
• OWASP Top 10 for LLMs
• Guidelines for secure AI system development

Security tools and techniques

API probing

• PrivacyRaven: runs different privacy attacks against ML models; the tool only runs black-box label-only attacks
• Counterfit: runs different adversarial ML attacks against ML models

Model backdoors

• Fickling: a decompiler, static analyzer, and bytecode rewriter for Python pickle files; injects backdoors into ML model files
• Semgrep rules for ML
• API Rate Limiting

Other

• Awesome Large Language Model Tools for Cybersecurity Research
• Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Background information

• Building A Generative AI Platform (Chip Huyen)
• Machine Learning Glossary | Google Developers
• Hugging Face NLP course
• Making Large Language Models work for you
• Andrej Karpathy's Intro to Large Language Models and Neural Networks: Zero to Hero
• Normcore LLM Reading List especially Building LLM applications for production
• 3blue1brown's Guide to Neural Networks
• Licensing:
  • From RAIL to Open RAIL: Topologies of RAIL Licenses
  • Hugging Face - OpenRAIL
  • Hugging Face - AI Release Models
  • Open LLMs
  • Prompt Engineering Guide

DeepFakes, disinformation, and abuse

• How to Prepare for the Deluge of Generative AI on Social Media
• Generative ML and CSAM: Implications and Mitigations

Notable incidents

Notable harms