CVE-2026-15531 in HashNeRF-pytorch
Summary
by MITRE • 07/13/2026
A vulnerability has been found in yashbhalgat HashNeRF-pytorch up to 82885e698295982504eb6a26d060a6b2473e3706. Affected by this issue is the function torch.load of the file run_nerf.py of the component Checkpoint File Handler. The manipulation of the argument ckpt_path leads to deserialization. The attack must be carried out locally. The exploit has been disclosed to the public and may be used. This product uses a rolling release model to deliver continuous updates. As a result, specific version information for affected or updated releases is not available. The pull request to fix this issue awaits acceptance.
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Analysis
by VulDB Data Team • 07/13/2026
This vulnerability exists within the HashNeRF-pytorch implementation maintained by yashbhalgat, specifically affecting the checkpoint file handling mechanism in run_nerf.py. The flaw manifests through the torch.load function which processes checkpoint paths without proper validation or sanitization of user-provided input. When an attacker can manipulate the ckpt_path argument during deserialization operations, they create a potential attack vector that could lead to arbitrary code execution or data corruption within the application environment.
The technical nature of this vulnerability aligns with common deserialization flaws categorized under CWE-502, which deals with deserialization of untrusted data. The issue represents a classic sandbox escape scenario where local privilege escalation opportunities exist due to the lack of input validation during checkpoint loading operations. This vulnerability operates at the intersection of software security and machine learning infrastructure, where model checkpoints are loaded without proper security boundaries.
The operational impact is significant for users running this codebase locally since the attack vector requires only local execution privileges. The vulnerability allows malicious actors to exploit the checkpoint loading mechanism by crafting specially crafted checkpoint files or manipulating file paths that ultimately get passed to torch.load. This creates a persistent threat within development environments where researchers and practitioners might unknowingly load compromised model checkpoints, potentially leading to code execution or data compromise.
Security mitigation strategies should focus on implementing proper input validation for all checkpoint paths before processing them through torch.load functions. The recommended approach involves sanitizing user inputs, implementing strict path validation checks, and ensuring that only trusted checkpoint files are loaded into the system. Additionally, adopting a principle of least privilege in development environments can help limit potential damage from successful exploitation attempts.
The vulnerability disclosure status indicates that this issue has been made public and could be actively exploited, making immediate mitigation necessary for affected users. Given the rolling release model employed by this project, tracking specific vulnerable versions becomes challenging but proper version pinning and security monitoring practices should be implemented to maintain system integrity. The fix awaiting acceptance in a pull request represents an important step toward remediation, though users should implement defensive measures immediately while waiting for official patches.
Organizations using this software should consider implementing additional security controls such as file integrity monitoring, automated vulnerability scanning of checkpoint files, and regular security assessments of machine learning infrastructure components. The ATT&CK framework categorizes this type of vulnerability under initial access and execution techniques where adversaries leverage application weaknesses to execute malicious code within trusted environments.
The broader implications extend beyond immediate exploitation risks as this vulnerability demonstrates the importance of secure coding practices in machine learning frameworks where checkpoint handling becomes a critical security boundary. Proper validation of serialized objects and strict adherence to security guidelines during development can prevent similar issues from emerging in other components of complex AI systems.