OS Credential Dumping - AI Security Technique
AI Security TechniqueAdversaries may extract credentials from OS caches, application memory, or other sources on a compromised system. Credentials are often in the form of a hash or clear text, and can include usernames and passwords, application tokens, or other authentication keys. Credentials can be used to perform Lateral Movement to access other AI services such as AI agents, LLMs, or AI inference APIs. Cre...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may extract credentials from OS caches, application memory, or other sources on a compromised system. Credentials are often in the form of a hash or clear text, and can include usernames and passwords, application tokens, or other authentication keys.
Credentials can be used to perform Lateral Movement to access other AI services such as AI agents, LLMs, or AI inference APIs. Credentials could also give an adversary access to other software tools and data sources that are part of the AI DevOps lifecycle.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0090
- Maturity
- demonstrated
- ATT&CK external ID
- T1003
- Priority score
- 30
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses0 ATLAS mitigation records
- Public examples1 linked case study records
- Research risks0 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
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 evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.