Security1 publisher2 min readPublished
Mandiant's testers talked an internal AI assistant into pushing private repos to their own GitHub
Mandiant's report describes an assistant that had permission to clone internal repositories and an approved domain to push them to, and an accounting agent whose runaway loop billed about $50,000 in under an hour.
The Watch · Security desk
What happened
- Mandiant's AI Risk and Resilience report warns that a poisoned data source, model dependency or extension hook can turn a trusted agent into a channel for internal reconnaissance, lateral movement or sandbox escape.
- Its offensive security testing puts prompt injection among the primary attack vectors in enterprise AI deployments, alongside improper file permissions and inadequate access controls.
- In one assessment, testers convinced an internal AI assistant that managed code repositories and CI/CD pipelines it was in an authorized security test, then supplied a personal access token for a GitHub repository they controlled.
- An accounting agent entered a runaway execution loop, made more than 15,000 high-cost API calls in less than an hour, generated about $50,000 in cloud charges and disrupted active business transactions.
- In March, Mandiant responded to supply chain compromises tied to UNC6780, also known as TeamPCP, which stole AI service credentials and used prompt injection against AI coding assistants and LLM-based security scanners.
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Why it matters
- capability An approved-domain policy stops being a boundary when the agent can authenticate to an account inside that domain: the push looks like sanctioned traffic and the reading of internal repositories uses access already granted.
- cost The runaway loop bills the operator directly, so the team that provisioned the agent carries a five-figure cloud exposure created by its own tooling.
- decision Detection has to sit on agent token use and cross-application API calls, which is what Mandiant asks for; an agent misusing permissions it holds stays inside domain and network policy.
- precedent With backdoored automation skills already found in circulation and a named group stealing AI service credentials, review of agent extensions and model dependencies becomes part of ordinary dependency review.
The exfiltration in that assessment ran on the assistant's own credentials. GitHub was an approved domain, so the destination passed policy, and the assistant cloned sensitive internal repositories and pushed them to the external account [5]. Mandiant says the test shows how an AI agent can be manipulated into abusing legitimate permissions [6].
A domain allowlist assumes the domain is the boundary. Here the agent could authenticate to an account inside an approved domain, using a token it had been handed [4][5].
The accounting agent ran up the bill on its own. Mandiant says weak controls can have consequences even without an attacker [8]. More than 15,000 calls in less than an hour works out to over 250 a minute [1]. Divide $50,000 by 15,000 and each call cost about $3.33, and less than that if the loop ran past 15,000 before someone stopped it [2]. The report presents that loop as one case study and does not give a rate for how often agents fail this way [3].
In February, VirusTotal found malicious OpenClaw skills disguised as legitimate automation packages, carrying backdoors, droppers, infostealers and remote access tools [9]. In May, GTIG disclosed what it described as the first publicly confirmed case of a cybercriminal using an AI-developed zero-day exploit in a planned mass exploitation campaign, against a flaw that could bypass two-factor authentication in a popular open-source administration tool [11]. Mandiant describes attackers adapting software supply chain techniques to AI tools [17].
GTIG has also observed threat actors using AI for vulnerability research, including persona-driven jailbreaking and specialized security datasets that support discovery and exploitation [13]. Some are building middleware, proxy relays and automated registration systems to get around safety guardrails and billing constraints on commercial AI platforms [12]. The billing constraints they are working around are the same platform metering that ran the accounting agent's charges to about $50,000 [7][12].
"Defending against these autonomous threats requires transitioning to clearly identified, adaptive identity controls, accelerating defensive velocity, and reorienting the SOC toward real-time behavioral telemetry," Mandiant said [14].
What that asks for in practice is telemetry on agent token use, cross-application API calls and application activity, because MCP servers, third-party APIs and dynamic agent instructions widen the surface further [15]. The report also asks organizations to extend secure development practices to AI engineering, keep inventories of models, applications and services, and use automated SBOMs to track components and dependencies from development into production [16].
What to watch
- Whether Mandiant's next report gives incident counts for agent abuse and runaway cost.
- Whether a second confirmed case of an AI-developed zero-day in a mass exploitation campaign follows GTIG's May disclosure.
- Whether more malicious agent skills surface in VirusTotal scanning after February's OpenClaw batch.