One Article Review
| Source |  Schneier on Security |
|---|---|
| Identifiant | 8647009 |
| Date de publication | 2025-02-05 12:03:01 (vue: 2025-02-05 12:07:58) |
| Titre | On Generative AI Security |
| Texte | Microsoft’s AI Red Team just published “Lessons from
Red Teaming 100 Generative AI Products.” Their blog post lists “three takeaways,” but the eight lessons in the report itself are more useful:
Understand what the system can do and where it is applied.
You don’t have to compute gradients to break an AI system.
AI red teaming is not safety benchmarking.
Automation can help cover more of the risk landscape.
The human element of AI red teaming is crucial.
Responsible AI harms are pervasive but difficult to measure.
LLMs amplify existing security risks and introduce new ones...
Microsoft’s AI Red Team just published “Lessons from Red Teaming 100 Generative AI Products.” Their blog post lists “three takeaways,” but the eight lessons in the report itself are more useful: Understand what the system can do and where it is applied. You don’t have to compute gradients to break an AI system. AI red teaming is not safety benchmarking. Automation can help cover more of the risk landscape. The human element of AI red teaming is crucial. Responsible AI harms are pervasive but difficult to measure. LLMs amplify existing security risks and introduce new ones... |
| Notes | ★★★★ |
| Envoyé | Oui |
| Condensat | “lessons “three 100 amplify applied are automation benchmarking blog break but can compute cover crucial difficult don’t eight element existing from generative gradients harms have help human introduce itself just landscape lessons lists llms measure microsoft’s more new not ones pervasive post products published red report responsible risk risks safety security system takeaways team teaming understand useful: what where |
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