Infostealers Pivot to AI: OpenClaw Agent Secrets Targeted in New Malware Wave

The cybersecurity landscape has reached a new inflection point as information-stealing malware begins to treat artificial intelligence infrastructure as a primary target. Recent reports indicate that multiple infostealer variants have been updated to specifically hunt for and exfiltrate configuration files and gateway tokens associated with OpenClaw, a prominent framework for agentic AI. This shift signals that threat actors are moving beyond traditional credential theft—such as browser cookies and saved passwords—to target the high-value secrets that power the next generation of autonomous enterprise software.

OpenClaw has seen rapid adoption across various sectors due to its ability to orchestrate complex tasks through AI agents. However, the very nature of these agents requires them to hold significant privileges, including API keys for Large Language Models (LLMs) and authentication tokens for internal databases and cloud services. By targeting the configuration files where these secrets are often stored, attackers can effectively hijack an organization's AI identity. This allows for unauthorized API usage, which can lead to massive financial costs, and provides a backdoor into the sensitive data the AI agent was designed to process.

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Recent reports indicate that multiple infostealer variants have been updated to specifically hunt for and exfiltrate configuration files and gateway tokens associated with OpenClaw, a prominent framework for agentic AI.

The technical execution of these attacks involves malware scanning for specific directories and file extensions linked to the OpenClaw environment. Once located, these files are bundled and sent to command-and-control (C2) servers. The theft of gateway tokens is particularly concerning. These tokens often act as a persistent session identifier, allowing an attacker to bypass standard authentication protocols and interact with AI gateways as if they were the legitimate agent. In many enterprise setups, these agents have broad read/write access to internal documentation and code repositories, making the impact of a compromise potentially catastrophic.

This development mirrors the historical evolution of infostealers. Just as malware evolved to target cryptocurrency wallets and VPN configurations as those technologies became mainstream, we are now seeing the birth of AI-native malware. Security researchers suggest that this is likely the first of many such campaigns. As more companies integrate agentic AI into their core workflows, the secret sprawl associated with these frameworks becomes an irresistible target for cybercriminals looking for the path of least resistance into corporate networks.

For CISOs and security architects, the targeting of OpenClaw serves as a stark reminder that AI security cannot be treated as a siloed concern. The traditional perimeter is effectively extended to every configuration file and environment variable used by an AI framework. Moving forward, organizations must prioritize the use of hardware security modules (HSMs) or dedicated secret management services to store AI credentials, rather than relying on local configuration files. Furthermore, monitoring for unusual API traffic patterns and implementing strict token rotation policies will be essential to mitigating the risks posed by this new breed of infostealer.

As the industry watches this situation unfold, the focus will likely shift toward how AI framework developers themselves can harden their software against local file theft. For now, the burden of defense lies with the end-users, who must recognize that their AI agents are now high-priority targets in the global cybercrime ecosystem. The transition from stealing personal data to stealing the operational brains of an enterprise's AI represents a sophisticated escalation in the ongoing arms race between malware developers and security professionals.