Cloud Service Discovery - AI Security Technique
AI Security TechniqueAdversaries may attempt to enumerate the cloud services running on a system after gaining access. These methods can differ from platform-as-a-service (PaaS), to infrastructure-as-a-service (IaaS), software-as-a-service (SaaS), or AI-as-a-service (AIaaS). Many services exist throughout the various cloud providers and can include Continuous Integration and Continuous Delivery (CI/CD), Lambda Functions, Entra ID, AI...
Overview
A source-backed snapshot of this AI security technique.
Adversaries may attempt to enumerate the cloud services running on a system after gaining access. These methods can differ from platform-as-a-service (PaaS), to infrastructure-as-a-service (IaaS), software-as-a-service (SaaS), or AI-as-a-service (AIaaS). Many services exist throughout the various cloud providers and can include Continuous Integration and Continuous Delivery (CI/CD), Lambda Functions, Entra ID, AI Inference, Generative AI, Agentic AI, etc. They may also include security services, such as AWS GuardDuty and Microsoft Defender for Cloud, and logging services, such as AWS CloudTrail and Google Cloud Audit Logs.
Adversaries may attempt to discover information about the services enabled throughout the environment. Azure tools and APIs, such as the Microsoft Graph API and Azure Resource Manager API, can enumerate resources and services, including applications, management groups, resources and policy definitions, and their relationships that are accessible by an identity. They may use tools to check credentials and enumerate the AI models available in various AIaaS providers' environments including AI21 Labs, Anthropic, AWS Bedrock, Azure, ElevenLabs, MakerSuite, Mistral, OpenAI, OpenRouter, and GCP Vertex AI [1].
References
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0075
- Maturity
- realized
- ATT&CK external ID
- T1526
- Priority score
- 40
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelrealized
- 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.
LLM Jacking
The Sysdig Threat Research Team discovered that malicious actors utilized stolen credentials to gain access to cloud-hosted large language models (LLMs). The actors covertly gathered information about which models were enabled on the cloud service and created a reverse proxy for LLMs that would allow them to provide model access to cybercriminals.
The Sysdig researchers identified tools used by the unknown actors that could target a broad range of cloud services including AI21 Labs, Anthropic, AWS Bedrock, Azure, ElevenLabs, MakerSuite, Mistral, OpenAI, OpenRouter, and GCP Vertex AI. Their technical analysis represented in the procedure below looked at at Amazon CloudTrail logs from the Amazon Bedrock service.
The Sysdig researchers estimated that the worst-case financial harm for the unauthorized use of a single Claude 2.x model could be up to $46,000 a day.
Update as of April 2025: This attack is ongoing and evolving. This case study only covers the initial reporting from Sysdig.
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.