Threats Tagged 'honeypot'

In this article AI workloads are becoming high-value control points Case study 1: LiteLLM gateway compromise Case study 2: RAGFlow compromise Case study 3: Kestra compromise Mitigation and protection guidance MITRE ATT&CK techniques observed References Learn more AI is creating a new layer of enterprise infrastructure. Gateways, retrieval platforms, orchestration services, and containerized runtimes now sit between users, applications, data, and models. These systems concentrate credentials, data access, model connectivity, and execution privileges, making them some of the most powerful components in the AI stack. That concentration of trust is also creating new opportunities for attackers. In recent investigations, Microsoft observed activity targeting three distinct AI workloads: a LiteLLM gateway, a RAGFlow deployment, and a Kestra workflow environment. The intrusion paths varied, but the objectives were strikingly similar. Attackers sought to steal credentials, establish persistence, and monetize compromised compute resources. The individual techniques matter, but the broader pattern matters more. Across these cases, attackers treated AI infrastructure as a control plane where credential theft, host compromise, and downstream data access can converge. As organizations continue to deploy AI systems, these platforms are becoming high value targets that deserve the same security scrutiny as other critical enterprise infrastructure. AI workloads are becoming high-value control points The campaign-level signal extends beyond one product. The targeted workloads served different functions, but each exposed assets that could support follow-on abuse, including model-provider keys, proxy-issued virtual keys, database connection strings, tenant configuration, workflow execution, or host compute. Post-compromise behavior varied by workload role. Defenders should inventory exposed AI management surfaces, restrict administrative access, and monitor for gateway-originated execution and secret access. Three observed compromises across AI workloads AI workload Observed activity Attacker objective LiteLLM Observed attacker activity : Python droppers, runtime secret harvesting, PostgreSQL collection, miner deployment, and persistence activity from the LiteLLM gateway context. Microsoft assessment: Initial access likely occurred through exploitation of the exposed LiteLLM gateway surface, consistent with the vulnerability chain involving CVE-2026-42271 and CVE-2026-48710. Credential theft, backend database access, durable host access, and compute monetization. RAGFlow Observed attacker activity : Possible SSRF-style reconnaissance followed several days later by code execution, application-path modification, and placement of a Python hook in the TenantLLM credential-configuration flow. Public research: Describes multiple RAGFlow execution paths; Microsoft does not attribute this intrusion to a specific vulnerability. Intercept newly configured LLM provider credentials and model metadata. Kestra Observed attacker activity : Workflow-origin shell execution, Docker and container-environment discovery, XMRig deployment, and follow-on data collection. Microsoft assessment: Initial access likely involved exploitation of the exposed Kestra orchestration surface, with CVE-2026-49869 providing relevant public vulnerability context. Secret discovery, container-level access, data collection, and rapid compute monetization. Case study 1: LiteLLM gateway compromise Framework role and affected runtime context LiteLLM is commonly deployed as a proxy or gateway between applications and model providers. In that position, the service may hold or retrieve model-provider keys, LiteLLM master keys, virtual-key records, database connection strings, routing configuration, and tenant policy data. Command execution in the gateway runtime therefore exposed a process context close to AI routing and credential material. Figure 1. LiteLLM gateway compromise – attack chain…

Between May and July 2026, security researchers deployed an unauthenticated Model Context Protocol (MCP) honeypot server to observe how threat actors exploit AI agent infrastructure. Of approximately 1,000 sources that reached the decoy, 596 spoke the protocol and 24 proceeded to actively exploit it. These operators executed 628 shell commands, 255 file reads, and 248 secrets-store lookups, with 19 hunting credentials and 4 attempting container escapes. Activity escalated from 39 tool calls in May to 877 by mid-July. Three stolen credentials were subsequently used against a live AWS account, with two cases involving Bedrock model invocation for LLMjacking. The attacks demonstrated automated reconnaissance, credential harvesting, container escape attempts, backdoor account creation, and Kubernetes enumeration, revealing that exposed MCP servers represent a growing attack surface as AI agent infrastructure proliferates.

A threat actor nicknamed ViciousTrap has compromised over 5,500 edge devices, transforming them into honeypots. The actor targets more than 50 brands of SOHO routers, SSL VPNs, DVRs, and BMC controllers, possibly to collect exploited vulnerabilities. The infection chain involves exploiting CVE-2023-20118 to deploy a script called NetGhost, which redirects incoming traffic to the attacker's infrastructure. The compromised devices, mostly end-of-life, are used to create a distributed honeypot-like network across Asia. The actor, likely of Chinese-speaking origin, may be attempting to observe exploitation attempts and collect non-public or zero-day exploits. The infrastructure uses servers in Malaysia, and the campaign has been ongoing since March 2025.

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