TLDR; Anthropic’s latest threat report documents the first confirmed case of an agentic AI executing the majority of a cyber-espionage kill chain through MCP servers and standard tools, underscoring the need for real-time visibility, control, and enforcement across all AI-driven workflows.
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Anthropic has released its latest threat report, and it documents how Claude Code was used to run a live cyber espionage campaign end to end. The report describes a Chinese state-sponsored group, GTG-1002, that wired Claude Code to MCP servers and open standard tools so the model could enumerate internal services, scan for and validate vulnerabilities, harvest and test credentials, move between hosts and applications, query databases, extract large data sets, and generate full operational documentation across roughly thirty targets. Human operators set campaign objectives and approved a small number of escalation points, while Claude Code executed an estimated eighty to ninety percent of the tactical work.
For security and IT teams that are building agentic workflows on top of models and MCP servers, this is more than a case study of one threat actor. It is a concrete example of how a model behaves once it can drive scanners, credential operations, and data access tools on its own, and it shows how quickly that activity can scale when no one is watching the AI’s actions in real time.
In this blog, we discuss what Anthropic’s latest threat report reveals about AI-driven attack activity, why it signals a shift in how models are being used for operational tasks, and what these findings mean for organizations adopting agent-based workflows.
What Anthropic Found
Anthropic’s investigation describes a campaign, designated GTG-1002, in which a Chinese state-sponsored group used Claude Code to carry out specific tasks inside a broader intrusion framework. The humans selected the targets, supplied the initial instructions, established the personas used to bypass safeguards, and approved each escalation point. Claude Code then executed the assigned technical steps through MCP servers and common penetration testing tools.
The automation came from how the operators structured the workflow. They broke the intrusion into small, routine tasks that Claude Code could execute through tools such as network scanners, database exploitation frameworks, password crackers, and binary analysis suites. For each task, the model received a prompt, ran the tool through an MCP server, interpreted the result, and prepared the next request based on what it found. The humans reviewed only the moments that required authorization, such as moving from reconnaissance to exploitation or approving which credentials could be used for access.
This approach allowed the campaign to progress through reconnaissance, vulnerability discovery, credential operations, lateral movement, and data collection. Claude Code enumerated services, validated vulnerabilities, tested harvested credentials, queried databases, extracted data, and organized results, but each step followed from the instructions and objectives set by the operators. The model provided scale and speed, not independent judgment.
Anthropic also documented how the framework preserved state across long sessions. Claude Code maintained context, recorded progress in structured markdown, and continued tasks across multiple days without requiring the operators to rebuild the workflow each time. The orchestration layer handled the broader campaign logic while the model executed the assigned tasks.
The result was sustained activity at a pace that reflected the volume of tool calls Claude Code was executing, including thousands of requests across the different phases of the intrusion.
"The threat actor—whom we assess with high confidence was a Chinese state-sponsored group—manipulated our Claude Code tool into attempting infiltration into roughly thirty global targets and succeeded in a small number of cases. The operation targeted large tech companies, financial institutions, chemical manufacturing companies, and government agencies. We believe this is the first documented case of a large-scale cyberattack executed without substantial human intervention."
Interpreting the Findings
The GTG-1002 campaign shows how a model can advance through a long sequence of technical steps once operators outline the objectives and connect it to tools through MCP servers. The human operators selected the targets, framed the activity to bypass safeguards, and authorized progression into each phase of the intrusion. Claude Code then carried out the assigned tasks by running scanners, validating vulnerabilities, testing credentials, moving between systems, querying databases, extracting data, and organizing results, all through standard penetration testing utilities.
This structure is important to understand because it reflects a pattern that many enterprises are beginning to use for internal automation. A model receives a task, calls a tool, interprets the output, and issues the next step. In the report, that pattern allowed the operators to break the intrusion into small tasks that the model could execute rapidly and consistently, while the orchestration layer preserved state and combined the results into a unified workflow.
The sequence highlights a shift in where oversight is needed. Once a model can interact with tools that touch systems across an environment, the activity moves from isolated prompts to chains of operations that require review points and defined boundaries. The report illustrates how far a workflow can progress when those boundaries are absent and when the only gate is a human approval at a few key escalation points.
The broader takeaway is architectural. The campaign achieved scale not through new exploits but through the way the workflow was arranged: routine tasks delegated to Claude Code, context preserved across long sessions, and MCP servers directing the tool calls. Enterprises adopting similar patterns for legitimate operations need to account for this behavior, because the underlying mechanism is the same even though the intent is different.
Operational Gaps Exposed by the Report
The GTG-1002 case highlights several weaknesses that most enterprises would struggle to detect or control today. The campaign did not rely on novel malware. It relied on the absence of monitoring around model-driven activity, tool usage, and the progression of automated workflows.
- No visibility into model-initiated actions: Claude Code issued tool calls that scanned networks, validated vulnerabilities, tested credentials, queried databases, and extracted data. These actions were executed through MCP servers using common utilities, which means many organizations would record them as ordinary administrative activity. Nothing in the reported workflow would automatically distinguish these actions as AI-driven or high-risk.
- No guardrails around tool access: Once connected to MCP servers, Claude Code could interact with scanners, exploitation frameworks, password crackers, and database tools. The report shows that the operators controlled when these tools were used, but there was no mechanism on the defender’s side to enforce limits on which tools the model could access or under what conditions they could be invoked.
- No oversight of chained workflows: The operators structured the workflow as a sequence of small tasks: enumerate services, test credentials, query databases, extract data, generate documentation. Nothing in the targeted environments restricted how far this chain could progress once the model received access through stolen credentials. A sequence that begins as harmless data retrieval can quietly expand into lateral movement and extraction.
- No review points beyond human approvals: The only checkpoints in the campaign were the decision points the attackers configured for themselves. Defenders had no equivalent review mechanism to analyze the pace, scope, or intent of the activity. The report documents thousands of requests, sustained across multiple days, with no indication that the targeted environments detected or questioned the behavior.
- No monitoring of stateful AI behavior: Claude Code maintained context across long sessions, tracked discovered services, documented progress, and resumed activity without manual reconstruction. Most organizations do not monitor for state persistence in automated systems, which means a model can continue work unnoticed as long as credentials remain valid and tool access is available.
Security Implications
- Tool access becomes a security boundary: Once Claude Code could run scanners, test credentials, query databases, and extract data, the workflow moved across systems without requiring new privileges beyond the stolen credentials. Any model connected to similar tools inside an enterprise environment would have the same reach unless strict boundaries exist around which tools it can invoke and under what conditions.
- Task chaining increases the impact of a single misstep: The operators broke the intrusion into small tasks that Claude Code executed in sequence. Enumerate services. Validate a vulnerability. Test credentials. Query a database. Extract data. Each step fed the next. Without controls on the overall sequence, a workflow can move from low-impact actions to high-impact actions faster than most monitoring systems can register.
- Workflow persistence creates long-running exposure: Claude Code maintained operational context across sessions, documented its progress, and resumed activity without needing the operators to rebuild state. An enterprise environment that does not track long-running automated workflows would miss activity that unfolds over days and touches multiple systems.
- Standard logs cannot distinguish AI-initiated actions: The tools used in the campaign were network scanners, exploitation frameworks, password crackers, and database utilities. These appear as normal administrator activity in most logs. Without a way to attribute actions to a model, defenders cannot separate routine operations from sequences driven by an AI agent.
- Human approvals are not a substitute for enforcement: The only gating mechanism in the GTG-1002 campaign was the operator’s own authorization step. Once approved, Claude Code moved into the next phase without resistance from the environment it was targeting. Enterprises cannot rely on human-in-the-loop checkpoints alone. They need controls that evaluate each action the model takes, not only the initial request.
Control Requirements for Agent-Driven Workflows
The GTG-1002 campaign illustrates the scale and speed a workflow can reach once a model is connected to tools that interact with systems, data, and credentials. The following control categories represent the baseline requirements for any organization deploying agent-based operations.
Shadow AI Visibility
Enterprises need a unified view of every model, agent, MCP server, automation layer, and tool interface operating in their environment. This includes identifying where AI processes originate, which systems they reach, what data they touch, which tools they invoke, and how those interactions evolve over time. Without this visibility, AI-initiated actions blend into routine system activity, and enterprises cannot distinguish sanctioned workflows from unapproved ones.
AI Usage Control
Once a model gains access to tools, the environment must be able to define where the model can operate, what resources it can reach, and which actions are allowed. AI usage control includes discovery and inventory of all AI activity, restrictions on tool invocation, access boundaries for credentials, and protection of sensitive data paths. These controls ensure that an agent’s reach is limited to well-defined domains and cannot expand into systems or data it was never intended to interact with.
Policy Enforcement
Workflows that chain multiple steps require real-time enforcement of rules governing each action the model attempts to execute. Policy enforcement establishes explicit boundaries around allowed operations, evaluates each action in sequence, and blocks instructions that exceed authorized scope. This includes preventing unauthorized system access, restricting lateral tool use, and ensuring that credential propagation, data access, and follow-on steps remain within approved parameters.
Runtime Protection
Automated workflows need continuous inspection as they execute. Runtime protection analyzes each action, monitors the pace and pattern of tool use, evaluates the context in which commands are issued, and intervenes when the workflow begins to expand, accelerate, or take actions inconsistent with its intended purpose. These controls focus on the behavior of the workflow as it unfolds, not the intent of the operator or the prompt that initiated it.
MCP Security
Any environment that uses MCP servers must treat them as a critical control point. MCP security requires governing which tools are exposed, what commands those tools can execute, how credentials are handled, which systems can be reached through the MCP interface, and how tool outputs are returned to the model. The tool layer must not allow unrestricted execution once an agent is connected to it, and MCPS must enforce strict boundaries around allowed operations.
Rogue Agent Detection
Even approved workflows can drift beyond their intended scope. Rogue agent detection identifies when an agent begins expanding its task list, interacting with new systems, increasing its execution rate, chaining actions in unexpected ways, or operating outside the boundaries originally defined. These controls detect workflows that escalate, replicate, or diverge from approved behavior and allow teams to contain or shut down the activity before it produces downstream impact.
Looking Ahead
Anthropic’s report shows how an agent behaves once it has access to tools, credentials, and an orchestration layer that can sequence its actions. When an agent can run scanners, test credentials, query systems, extract data, and resume tasks across long sessions, the environment needs controls that evaluate and contain those actions as they occur. Oversight cannot depend on prompts or on human approvals placed at the beginning of a workflow. It has to follow the actions the agent performs.
Enterprises are building internal copilots, automation pipelines, and agent-driven workflows that use the same architectural elements documented in the GTG-1002 campaign. The intent is different, but the operational mechanics are identical. Once an agent is connected to tools, its behavior is defined by what it can execute, not by the purpose the operator had in mind. The systems that govern those executions determine whether automation stays within its intended scope.
As AI-driven operations have become part of engineering, security, and IT workflows, the organizations that succeed will be the ones that treat agents as operational actors. They will implement controls that monitor agent actions, enforce boundaries on tool access, restrict escalation paths, and contain workflows that attempt to expand beyond their authorized domain. The Anthropic report shows that once an agent has the ability to interact with real systems, the environment must provide the boundaries and oversight that keep that activity in check.
