AI Security: Prompt Injection and Tool Abuse Explained
AI Security: leave with an injection test suite, least-privilege tool config, output sanitization and an audit log.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why ai security matters — the common problem: AI features trust retrieved content and tool outputs, opening doors to injection and data leaks.
- 2Core concepts in plain language: Prompt injection, untrusted retrieved content, sensitive-data exposure, excessive tool permissions, insecure output handling, model extraction risks
- 3Going further: supply-chain risks, sandboxing, approval boundaries, auditability
- 4Framework walkthrough: NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, Model Cards
- 5Practical workflow, built live: An injection test suite, least-privilege tool config, output sanitization and an audit log.
- 6How to measure it: Regression-eval pass rate, Drift alerts, Security findings, Incident count
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- An injection test suite, least-privilege tool config, output sanitization and an audit log.
- A working understanding of NIST AI Risk Management Framework and OWASP Top 10 for LLM Applications
- A short list of measures to track: Regression-eval pass rate, Drift alerts, Security findings
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