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A threat actor claiming to be a bug bounty hunter likely used Anthropic’s Claude Code to automate large parts of an intrusion against a financial services organization, according to researchers investigating the incident.

The attack is another example of how AI coding agents can be repurposed beyond software development. Instead of simply generating scripts or explaining vulnerabilities, the AI appears to have assisted with reconnaissance, exploitation, credential theft, lateral movement, and data discovery during an actual compromise. (thehackernews.com)

Security researchers at ReliaQuest discovered the intrusion after identifying suspicious activity associated with a Linux system inside the victim’s environment.

Evidence suggested the attacker was using an AI-powered command-line environment consistent with Claude Code. The activity occurred at a pace that would have been difficult for a human operator to sustain manually.

The attacker reportedly used the system to enumerate infrastructure, search for credentials, investigate internal services, and generate commands dynamically as new information became available.

This allowed the intrusion to evolve quickly based on what the attacker discovered.

Rather than following a completely predefined script, the operation showed characteristics of an agentic workflow, where the AI could analyze results and determine useful next steps.

Once inside the environment, the attacker performed reconnaissance to understand the organization’s infrastructure and identify valuable systems.

Credentials became a major target.

The operation involved searching files and configuration data for authentication information that could provide additional access. The attacker then used discovered credentials to move deeper into the environment and interact with other systems.

This is particularly significant because valid credentials can allow malicious activity to blend with legitimate administrative behavior.

Traditional security tools may easily detect obvious malware, but an authenticated account using normal utilities and legitimate protocols can be considerably harder to distinguish from an employee or administrator.

When confronted, the attacker reportedly claimed to be conducting security research as part of a bug bounty effort.

However, bug bounty programs operate within explicitly defined scopes and authorization rules. Accessing systems outside that scope, moving through internal infrastructure, or retrieving sensitive information cannot automatically be justified simply by describing the activity as security research.

The researchers investigating the incident concluded that the behavior was more consistent with unauthorized intrusion activity.

The case highlights a growing problem as AI lowers the technical barrier for offensive security operations. Attackers may be able to automate activities that previously required significant manual expertise while presenting themselves as researchers after being discovered.

The most important aspect of the incident is not necessarily that AI introduced a completely new hacking technique.

Most of the underlying behaviors — reconnaissance, credential discovery, privilege escalation, lateral movement, and data access — have existed for years.

What AI changes is speed and scalability.

A coding agent can inspect command output, generate the next command, analyze configuration files, write scripts, search large amounts of information, and adapt its strategy without requiring the human operator to manually perform every step.

This allows a relatively small number of attackers to potentially conduct operations that previously required substantially more time and expertise.

The human operator can increasingly focus on objectives while the AI handles portions of execution.

Machine-speed attacks create a difficult problem for security operations teams.

Traditional incident response often involves an alert being generated, an analyst reviewing it, additional evidence being collected, and eventually a containment decision being made.

An AI-assisted attacker may continue enumerating systems, stealing credentials, and moving laterally during that investigation.

Organizations therefore increasingly need automated detection and containment capabilities capable of responding on similar timescales.

Identity monitoring becomes especially important. Security teams need visibility not only into whether authentication succeeds, but whether the subsequent behavior makes sense for that identity.

Endpoint telemetry, command execution patterns, unusual credential access, abnormal repository activity, and unexpected connections between systems can provide additional signals.

Claude Code and similar tools are designed primarily to help developers understand codebases, execute commands, debug applications, and automate software engineering tasks.

Those same capabilities naturally overlap with cybersecurity.

An agent capable of navigating a filesystem, executing shell commands, analyzing source code, interacting with services, and writing scripts already possesses many of the basic capabilities required for penetration testing.

The difference between legitimate testing and malicious intrusion therefore increasingly depends on authorization, permissions, technical safeguards, and how the tools are being used.

This incident provides another indication that agentic AI is becoming part of real-world cyber operations.

Attackers do not necessarily need AI to discover entirely new vulnerabilities. Automating the thousands of small decisions and commands involved in an intrusion may already be enough to significantly change how quickly cyberattacks can unfold.