GPT Academic: Deserialization of Untrusted Data
CVE-2026-0762 is a high-severity deserialization of untrusted data vulnerability in GPT Academic, with a CVSS 3.0 base score of 8.1 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected GPT Academic versions: 3.91. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.78% chance of exploitation within 30 days, higher than 56% of scored CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of GPT Academic you run against the affected versions below, and install the vendor's latest release.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| GPT Academic | GPT Academic | 3.91 | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- HighSuccess depends on conditions outside the attacker's control.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.0/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H. Scored by CNA (trendmicro.com).
Official description
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.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
How developers prevent this class of flaw
- If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
- When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
- Explicitly define a final object() to prevent deserialization.
More Deserialization of Untrusted Data vulnerabilities (CWE-502)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
- zerodayinitiative.com/advisories/ZDI-26-028/Mitigation, Third Party Advisory
Other vulnerabilities in GPT Academic
- 9.8 Critical severity
- 9.8 Critical severity
Recent Deserialization of Untrusted Data vulnerabilities
- 9.8 Critical severity
- 7.5 High severity
- 9.8 Critical severity
- 7.8 High severity
- 7.8 High severity
- 7.8 High severity
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