Adversarial testing evaluates how well LLMs resist malicious or unexpected inputs to ensure safety and security.
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Class IT 2024
Class Basic
A systematic methodology combining adversarial testing, risk analysis, and remediation to secure AI models and pipelines.
Map prompts, tools, plugins, and data connections exposed to user input.
Run jailbreak, prompt injection, and policy bypass scenarios.
Test moderation, permission boundaries, and output filtering behavior.
Findings: transcripts; remediation: guardrails, validation, architecture
Executive summary plus detailed technical findings, including full adversarial prompt transcripts and attack chains.
Findings mapped to OWASP LLM Top 10, Agentic AI Top 10, and MITRE ATLAS for structured AI risk classification.
Control-level guidance to harden prompts and guardrails for AI engineering teams.
Risk-prioritized remediation backlog maps findings to AI and security teams
LLM engagement assessed via OWASP and MITRE security frameworks continuously
Adversarial findings mapped with remediation ownership across AI security teams
Clear remediation ownership assigned across AI engineering and security teams for fixes
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