CVE-2024-0959 in GibsonEnv
Summary
by MITRE • 01/27/2024
A vulnerability was found in StanfordVL GibsonEnv 0.3.1. It has been classified as critical. Affected is the function cloudpickle.load of the file gibson\utils\pposgd_fuse.py. The manipulation leads to deserialization. It is possible to launch the attack remotely. The complexity of an attack is rather high. The exploitability is told to be difficult. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-252204.
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Analysis
by VulDB Data Team • 02/19/2024
The vulnerability identified as CVE-2024-0959 represents a critical deserialization flaw within the StanfordVL GibsonEnv 0.3.1 software package, specifically targeting the cloudpickle.load function located in the gibson\utils\pposgd_fuse.py file. This issue falls under the broader category of insecure deserialization vulnerabilities that have been consistently flagged by cybersecurity frameworks including CWE-502, which classifies deserialization of untrusted data as a critical security weakness. The vulnerability exists within a reinforcement learning environment designed for robotic simulation and training, making it particularly concerning given the sophisticated nature of the software ecosystem it operates within.
The technical exploitation of this vulnerability occurs through manipulation of serialized data that gets processed by the cloudpickle.load function, creating a pathway for arbitrary code execution. Cloudpickle is a library that extends pickle functionality to handle complex Python objects, but when used without proper input validation or sandboxing mechanisms, it becomes susceptible to malicious payload injection. The attack vector is particularly dangerous because it can be executed remotely, allowing threat actors to compromise systems without physical access. This remote exploit capability aligns with ATT&CK framework techniques such as T1203 (Exploitation for Client Execution) and T1059 (Command and Scripting Interpreter), where adversaries leverage application vulnerabilities to execute malicious code.
The operational impact of this vulnerability extends beyond simple code execution, as it can potentially allow attackers to gain full control over systems running affected software. In the context of robotic simulation environments like GibsonEnv, this could lead to unauthorized access to training data, manipulation of simulation parameters, or even control of physical robotic systems if the simulation environment is connected to real hardware. The high complexity and difficult exploitability rating suggests that while the vulnerability is serious, it requires significant expertise to exploit successfully, though the public disclosure of the exploit (VDB-252204) reduces the barrier to entry for malicious actors. Organizations utilizing this software must consider the potential for supply chain attacks, as the vulnerability could be exploited through compromised dependencies or development environments.
Mitigation strategies for CVE-2024-0959 should focus on immediate remediation through software updates from the vendor, as well as implementing defensive measures such as input validation and sandboxing of deserialization operations. Organizations should consider network segmentation to limit access to affected systems and implement monitoring for suspicious deserialization activities. The vulnerability highlights the importance of secure coding practices in machine learning and simulation environments, particularly when handling untrusted data inputs. Security teams should also conduct thorough vulnerability assessments of their software supply chains to identify similar issues in other dependencies, as this type of deserialization vulnerability is prevalent across many software ecosystems. The public disclosure of the exploit underscores the urgency of applying patches and implementing additional security controls to protect against potential exploitation attempts that may already be occurring in the wild.