GPT Academic stream_daas Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of GPT Academic. Interaction with a malicious DAAS server is required to exploit this vulnerability but attack vectors may vary depending on the implementation.
The specific flaw exists within the stream_daas function. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27956.
Weakness
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
Affected Software
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Gpt_academic | Binary-husky | 3.91 (including) | 3.91 (including) |
Potential Mitigations
- Make fields transient to protect them from deserialization.
- An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.