Security, privacy, and fairness · MLD-0036
Adversarial robustness
Resistance to adversarially chosen inputs or perturbations within a specified threat model.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Evaluate attacks with an explicit perturbation budget.
Original caveat
Robustness claims are only meaningful for the tested threat model.
Context and aliases
Level: Intermediate. Stages: Data · Training · Evaluation · Deployment · Governance.
Tags: ML · DL · SLM · LLM · Security · Privacy · Safety.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Adversarial Robustness Toolbox — Toolkit for adversarial ML evaluation and defenses.
- Captum — PyTorch model-interpretability library.
- Fairlearn — Tools for assessing and mitigating group fairness issues.
- garak — Language-model vulnerability probing toolkit.
- LIME — Local surrogate-model explanation toolkit.
- NeMo Guardrails — Toolkit for programmable controls around conversational applications.
- NNsight — Library for inspecting and intervening in neural-network computation.
- PyRIT — Python framework for AI red-team automation.
- pyvene — Library for interventions on model representations.
- SAELens — Tooling for training and analyzing sparse autoencoders.
- SHAP — Additive feature-attribution methods and tooling.
- TransformerLens — Framework for inspecting and intervening on supported Transformer internals.
Original references
- The Algorithmic Foundations of Differential Privacy ↗ · Reference link
- Google Machine Learning Glossary ↗ · Consulted
- OWASP Top 10 for LLM Applications ↗ · Reference link
- Original reference URL ↗