AI Security & Adversarial Robustness

Protect Large Language Models (LLMs) and Machine Learning (ML) systems from threats like prompt injection, data poisoning, model theft, and misuse. Strengthening the supply chain, inference processes, and monitoring mechanisms is crucial for defense.

Prompt injection: malicious instructions embedded within user input or retrieved contextual information.

Supply chain: compromised models, dependencies, or artifacts.

Input filtering techniques, such as MIME/type allowlists and HTML/script stripping, are employed to mitigate risks associated with malicious input. Safety classifiers and jailbreak detectors further safeguard against unwanted behavior.

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Citation-only answers for factual tasks

To ensure accuracy in factual task responses, JSON schema validation and post-filters are utilized to verify information. Watermarking or trace IDs are also implemented to facilitate auditability and accountability.

Frequently asked questions

What is vulnerability scanning for containers and libraries?

Vulnerability scanning for containers and libraries; patch cadence.

What access controls should be implemented on prompts, datasets, and secrets?

Access control on prompts, datasets, and secrets.

What monitoring and response strategies are necessary for AI security?

Monitoring & Response

How should prompts, context IDs, and outputs be logged and redacted?

Log prompts, context IDs, outputs (redacted) with request IDs.

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Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation

AI Security & Adversarial Robustness | ML Knowledge Hub