Adversaries may obtain access to generative artificial intelligence tools, such as large language models (LLMs), to aid various techniques during targeting. These tools may be used to inform, bolster, and enable a variety of malicious tasks, including conducting Reconnaissance, creating basic scripts, assisting social engineering, and even developing payloads.(Citation: MSFT-AI)
For example, by utilizing a publicly available LLM an adversary is essentially outsourcing or automating certain tasks to the tool. Using AI, the adversary may draft and generate content in a variety of written languages to be used in Phishing/Phishing for Information campaigns. The same publicly available tool may further enable vulnerability or other offensive research supporting Develop Capabilities. AI tools may also automate technical tasks by generating, refining, or otherwise enhancing (e.g., Obfuscated Files or Information) malicious scripts and payloads.(Citation: OpenAI-CTI) Finally, AI-generated text, images, audio, and video may be used for fraud, Impersonation, and other malicious activities.(Citation: Google-Vishing24)(Citation: IC3-AI24)(Citation: WSJ-Vishing-AI24)
Detection of Artificial Intelligence
Pre-compromise: Pre-compromise mitigations involve proactive measures and defenses implemented to prevent adversaries from successfully identifying and exploiting weaknesses during the Reconnaissance and Resource Development phases of an attack. These activities focus on reducing an organization's attack surface, identify adversarial preparation efforts, and increase the difficulty for attackers to conduct successful operations. This mitigation can be implemented through the following measures:
Limit Information Exposure:
Protect Domain and DNS Infrastructure:
External Monitoring:
Threat Intelligence:
Content and Email Protections:
Training and Awareness:
No cross-framework mappings available