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Home/CyberSecurity News/Hugging Face LeRobot Flaw Allows Un Vulnerability Enables
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Hugging Face LeRobot Flaw Allows Un Vulnerability Enables

LeRobot, Hugging Face’s widely used open-source machine learning framework for real-world robotics, is currently affected by a critical, unpatched remote code execution (RCE) vulnerability. Tracked...

David kimber
David kimber
April 29, 2026 2 Min Read
0 0

LeRobot, Hugging Face’s widely used open-source machine learning framework for real-world robotics, is currently affected by a critical, unpatched remote code execution (RCE) vulnerability.

Tracked as CVE-2026-25874 with a critical CVSS score of 9.3, the flaw allows unauthenticated attackers to execute arbitrary system commands on vulnerable host machines.

With nearly 24,000 stars on GitHub, this vulnerability poses a severe risk to AI infrastructure, connected robots, and sensitive proprietary data.

Insecure Pickle Deserialization

According to the detailed proof-of-concept published by Chocapikk, shows the flaw in the async inference module, which offloads heavy computations to a GPU server.

The PolicyServer and RobotClient components use Python’s native pickle module to deserialize data transmitted over unauthenticated gRPC channels.

Because the gRPC server uses add_insecure_port() without Transport Layer Security (TLS) or authentication, anyone with network access can connect directly to the service.

By sending a maliciously crafted serialized payload via RPC handlers such as SendPolicyInstructions or SendObservations, attackers can trigger automatic arbitrary code execution.

The malicious payload executes immediately during the pickle.loads() process, long before the system performs any data type validation.

Exploiting this vulnerability requires no credentials and no complex attack chains.

Because AI inference servers typically run with elevated system privileges to manage expensive GPU resources and massive datasets, a successful breach is devastating.

Attackers could gain complete administrative control over the host machine.

They can move laterally across the internal network, corrupt machine learning models, exfiltrate Hugging Face API keys, and potentially sabotage the physical operations of connected robots.

The vulnerability actively affects LeRobot versions up to 0.5.1..

Chocapikk security researchers emphasized a glaring irony in this codebase: Hugging Face originally developed the safetensors format specifically to eliminate the severe security risks associated with pickle serialization.

Despite creating the safe alternative, LeRobot developers used the unsafe pickle format for convenience.

Furthermore, Chocapikk discovered the source code contained # nosec tags directly next to the pickle.loads() calls.

These comments were deliberately placed to suppress automated security linter warnings that accurately flagged the vulnerability during development.

Mitigation Strategies

A permanent patch replacing pickle with safetensors and JSON is planned for LeRobot version 0.6.0

Until this official fix is deployed, organizations must implement immediate defensive measures:

  • Restrict network access to ensure the LeRobot async inference server is never exposed to untrusted networks or the public internet.
  • Bind the server strictly to localhost rather than 0.0.0.0 to block all external connection attempts.
  • Implement strong API gateways, VPNs, and network-level firewalls to enforce strict authentication before traffic reaches the gRPC port.

Disclaimer: HackersRadar reports on cybersecurity threats and incidents for informational and awareness purposes only. We do not engage in hacking activities, data exfiltration, or the hosting or distribution of stolen or leaked information. All content is based on publicly available sources.

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David kimber

David kimber

David is a penetration tester turned security journalist with expertise in mobile security, IoT vulnerabilities, and exploit development. As an OSCP-certified security professional, David brings hands-on technical experience to his reporting on vulnerabilities and security research. His articles often feature detailed technical analysis of exploits and provide actionable defense recommendations. David maintains an active presence in the security research community and has contributed to multiple open-source security tools.

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