Vertex AI Experiments: Generation of Predictable Numbers or Identifiers
CVE-2026-2473 is a high-severity generation of predictable numbers or identifiers vulnerability in Vertex AI Experiments from Google Cloud, with a CVSS 4.0 base score of 7.7 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, but only if a user takes part, for example by opening a link or file. The record lists version 1.133.0 as the first without the flaw, so updating Vertex AI Experiments to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 0.51% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Update Vertex AI Experiments to 1.133.0 or a later version.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
- Because the attack needs a user to take part, warn users about unexpected links and files related to this product until it is patched.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Google Cloud | Vertex AI Experiments | 1.21.0 up to, but not including, 1.133.0 | 1.133.0 |
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.
- Attack requirements
- PresentThe target must be in a specific state or configuration for the attack to work.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- PassiveA user only needs to do something routine, such as viewing a page.
- Confidentiality (vulnerable system)
- HighSerious loss of confidentiality on the vulnerable system.
- Integrity (vulnerable system)
- HighSerious loss of integrity on the vulnerable system.
- Availability (vulnerable system)
- HighSerious loss of availability on the vulnerable system.
- Confidentiality (other systems)
- NoneNo loss of confidentiality on systems beyond the vulnerable one.
- Integrity (other systems)
- NoneNo loss of integrity on systems beyond the vulnerable one.
- Availability (other systems)
- NoneNo loss of availability on systems beyond the vulnerable one.
Vector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:Clear. Scored by CNA.
Official description
Predictable bucket naming in Vertex AI Experiments in Google Cloud Vertex AI from version 1.21.0 up to (but not including) 1.133.0 on Google Cloud Platform allows an unauthenticated remote attacker to achieve cross-tenant remote code execution, model theft, and poisoning via pre-creating predictably named Cloud Storage buckets (Bucket Squatting). This vulnerability was patched and no customer action is needed.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Generation of Predictable Numbers or Identifiers
The product uses a scheme that generates numbers or identifiers that are more predictable than required.
More Generation of Predictable Numbers or Identifiers vulnerabilities (CWE-340)
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
Other references
Recent Generation of Predictable Numbers or Identifiers vulnerabilities
- 9.8 Critical severity
- 7.5 High severity
- 7.5 High severity
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