mlsanityData to 7 Oct 2026

CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models

arXiv 2208.07476 ↗First posted 16 Aug 2022cs.CR cs.AI cs.LGcited by 1

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

As the practicality of Artificial Intelligence (AI) and Machine Learning (ML) based techniques grow, there is an ever increasing threat of adversarial attacks. There is a need to red team this ecosystem to identify system vulnerabilities, potential threats, characterize properties that will enhance system robustness, and encourage the creation of effective defenses. A secondary need is to share this AI security threat intelligence between different stakeholders like, model developers, users, and AI/ML security professionals. In this paper, we create and describe a prototype system CTI4AI, to overcome the need to methodically identify and share AI/ML specific vulnerabilities and threat intelligence.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 2 of its 10 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

2 of 10 references.

References · 2

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287

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