invoke-ai/invokeai: Deserialization of Untrusted Data
CVE-2024-12029 is a critical-severity deserialization of untrusted data vulnerability in invoke-ai/invokeai from Invoke-ai, with a CVSS 3.0 base score of 9.8 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. The record lists version 5.4.3 as the first without the flaw, so updating invoke-ai/invokeai to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 5.98% chance of exploitation within 30 days, which is in the top 6.58% of all scored CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Update invoke-ai/invokeai to 5.4.3 or a later version.
- 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 |
|---|---|---|---|
| Invoke-ai | invoke-ai/invokeai | before 5.4.3 | 5.4.3 |
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
- LowNo special conditions are needed; the attack works reliably.
- 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:L/PR:N/UI:N/S:U/C:H/I:H/A:H. Scored by CNA (huntr.dev).
Official description
A remote code execution vulnerability exists in invoke-ai/invokeai versions 5.3.1 through 5.4.2 via the /api/v2/models/install API. The vulnerability arises from unsafe deserialization of model files using torch.load without proper validation. Attackers can exploit this by embedding malicious code in model files, which is executed upon loading. This issue is fixed in version 5.4.3.
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: poc, automatable: yes, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Patches and release notes
Other references
Other vulnerabilities in invoke-ai/invokeai
- 9.8 Critical severity
- 7.5 High severity
- 9.1 Critical severity
- 7.5 High severity
Recent Deserialization of Untrusted Data vulnerabilities
- 7.2 High severity
- 9.8 Critical severity
- 9.8 Critical severity
- 9.8 Critical severity
- 9.8 Critical severity
- 9.8 Critical severity
Spotted an error? Tell us.