CVE-2026-85704 in freegpt-webui
Summary
by MITRE • 09/04/2026
A security flaw has been discovered in ramon-victor freegpt-webui up to 098db3dfeb41555c2ca9269df0f13e10ec1c35dc. This issue affects the function getJailbreak of the file server/config.py of the component Jailbreak Mode. The manipulation results in race condition. It is possible to launch the attack remotely. The attack requires a high level of complexity. The exploitability is assessed as difficult. The exploit has been released to the public and may be used for attacks. This product takes the approach of rolling releases to provide continious delivery. Therefore, version details for affected and updated releases are not available. This vulnerability only affects products that are no longer supported by the maintainer.
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Analysis
by VulDB Data Team • 09/04/2026
The identified security flaw resides within the freegpt-webui project maintained by ramon-victor, specifically affecting versions up to commit 098db3dfeb41555c2ca9269df0f13e10ec1c35dc. This vulnerability is located in the getJailbreak function within the server/config.py file and pertains to the Jailbreak Mode component of the application. The core technical nature of this flaw is a race condition, which arises when the system fails to properly synchronize concurrent operations, allowing an attacker to manipulate the state of the application during critical execution windows. This type of vulnerability typically involves timing attacks where the outcome depends on the sequence or timing of uncontrollable external events, such as multiple requests arriving simultaneously. In this specific context, the race condition likely allows for unauthorized access or bypassing of intended security controls within the jailbreak functionality, potentially enabling users to execute restricted commands or access data they should not be able to reach under normal operational constraints.
From an operational perspective, this vulnerability presents a significant risk because it can be exploited remotely by an attacker over a network connection without requiring prior authentication in some scenarios, although the complexity of exploitation is assessed as high. The difficulty in exploiting this flaw stems from the precise timing required to trigger the race condition successfully, which demands sophisticated attack techniques and potentially multiple attempts to achieve success against a properly configured system. Despite the high complexity, the fact that an exploit has been released publicly increases the threat landscape significantly. Publicly available exploits lower the barrier to entry for less skilled attackers who might leverage existing tools or scripts to attempt this race condition attack, thereby increasing the likelihood of successful compromise in environments where the software is still deployed and monitored inadequately.
The impact of this vulnerability extends beyond simple unauthorized access due to the nature of jailbreak modes in web interfaces designed for AI interaction. If successfully exploited, an attacker could potentially bypass content filters or safety guidelines implemented by the application, leading to the generation of harmful, illegal, or malicious content through the integrated language model interface. This undermines the integrity and intended use case of the software, which is often used as a gateway to large language models with specific behavioral constraints. The ability to manipulate these constraints via a race condition means that security controls relying on sequential validation checks may be circumvented, resulting in a complete failure of the application's defensive posture regarding content moderation and access control.
It is crucial to note that this vulnerability affects products that are no longer supported by the maintainer. The project utilizes a rolling release model for continuous delivery, which means there are no discrete version numbers or official patches released after the final commit mentioned. Consequently, users of these unsupported versions cannot rely on vendor-provided fixes and must implement their own mitigations or migrate to alternative solutions that receive active security maintenance. This lack of support significantly amplifies the risk profile, as any discovered flaws remain unpatched indefinitely unless addressed by the community or third-party forks. Organizations relying on this software should treat it as deprecated from a security standpoint and prioritize migration away from these unsupported versions immediately.
To mitigate the risks associated with this race condition vulnerability in unsupported environments, administrators must implement compensating controls at the infrastructure level since code-level fixes are unavailable. Network segmentation can help limit exposure by restricting access to the application only through trusted networks or via secure gateways that enforce strict rate limiting and request validation. Implementing Web Application Firewalls configured to detect anomalous timing patterns or high-frequency requests targeting the jailbreak endpoint may also reduce the success probability of race condition attacks. Additionally, since the software is unsupported, the most effective mitigation strategy is complete decommissioning of the vulnerable instance followed by migration to a maintained alternative that adheres to secure coding practices and receives regular security updates aligned with industry standards such as OWASP guidelines for input validation and concurrency handling.