Record summary
A quick snapshot of what this page covers.
Attack context
How this AI attack works in practice.
Adversaries may use stolen application access tokens to bypass the typical authentication process and access restricted accounts, information, or services on remote systems. These tokens are typically stolen from users or services and used in lieu of login credentials.
Application access tokens are used to make authorized API requests on behalf of a user or service and are commonly used to access resources in cloud, container-based applications, software-as-a-service (SaaS), and AI-as-a-service(AIaaS). They are commonly used for AI services such as chatbots, LLMs, and predictive inference APIs.
- ATLAS ID
- AML.T0091.000
- ATT&CK external ID
- T1550.001
- Priority score
- 30
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
AIKatz: Attacking LLM Desktop Applications
Researchers at Lumia have demonstrated that it is possible to extract authentication tokens from the memory of LLM Desktop Applications. An attacker could then use those tokens to impersonate as the victim to the LLM backed, thereby gaining access to the victim’s conversations as well as the ability to interfere in future conversations. The attacker’s access would allow them the ability to directly inject prompts to change the LLM’s behavior, poison the LLM’s context to have persistent effects, manipulate the user’s conversation history to cover their tracks, and ultimately impact the confidentiality, integrity, and availability of the system. The researchers demonstrated this on Anthropic Claude, Microsoft M365 Copilot, and OpenAI ChatGPT.
Vendor Responses to Responsible Disclosure:
- Anthropic (HackerOne) - Closed as informational since local attack.
- Microsoft Security Response Center - Attack doesn’t bypass security boundaries for CVE.
- OpenAI (BugCrowd) - Closed as informational and noted that it’s up to Microsoft to patch this behavior.
Source
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Original source
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