AiDex: Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism
CVE-2025-3578 is a critical-severity inadequate detection or handling of adversarial input perturbations in automated recognition mechanism vulnerability in AiDex, with a CVSS 4.0 base score of 9.3 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 1.7 as the first without the flaw, so updating AiDex to that release or later removes the exposure. There is no confirmed exploitation so far. EPSS estimates a 0.5% chance of exploitation within 30 days, which is low compared with other CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Update AiDex to 1.7 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
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
- NoneThe attack does not depend on a particular deployment or configuration.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- 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)
- LowLimited 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:N/PR:N/UI:N/VC:H/VI:H/VA:L/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:X. Scored by CNA (incibe.es).
Official description
A malicious, authenticated user in Aidex, versions prior to 1.7, could list credentials of other users, create or modify existing users in the application, list credentials of users in production or development environments. In addition, it would be possible to cause bugs that would result in the exfiltration of sensitive information, such as details about the software or internal system paths. These actions could be carried out through the misuse of LLM Prompt (chatbot) technology, via the /api/<string-chat>/message endpoint, by manipulating the contents of the ‘content’ parameter.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism
The product uses an automated mechanism such as machine learning to recognize complex data inputs (e.g. image or audio) as a particular concept or category, but it does not properly detect or handle inputs that have been modified or constructed in a way that causes the mechanism to detect a different, incorrect concept.
How developers prevent this class of flaw
- Algorithmic modifications such as model pruning or compression can help mitigate this weakness. Model pruning ensures that only weights that are most relevant to the task are used in the inference of incoming data and has shown resilience to adversarial perturbed data.
- Consider implementing adversarial training, a method that introduces adversarial examples into the training data to promote robustness of algorithm at inference time.
- Consider implementing model hardening to fortify the internal structure of the algorithm, including techniques such as regularization and optimization to desensitize algorithms to minor input perturbations and/or changes.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: yes, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Other vulnerabilities in AiDex
- 9.3 Critical severity
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