CVE-2025-0190
Aimstack Aim 3.25.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2025-0190 is a high-severity Excessive Data Query Operations in a Large Data Table (CWE-1049) vulnerability in Aimstack Aim. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SC-6 (Resource Availability) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-0190 is a denial of service vulnerability affecting version 3.25.0 of aimhubio/aim. The flaw occurs when a large number of Text objects are tracked and then queried simultaneously through the web API, causing the Aim web server to become unresponsive to other requests for an extended period while processing and returning these objects. It is rated with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and is associated with CWE-1049 and NVD-CWE-Other.
The vulnerability can be exploited by any unauthenticated attacker with network access to the Aim web server, requiring low complexity and no user interaction. By repeatedly tracking large volumes of Text objects and issuing simultaneous queries via the web API, an attacker can render the server unresponsive, leading to a complete denial of service.
Mitigation details and additional information are available in the advisory published on Huntr at https://huntr.com/bounties/38d151f1-abb4-443a-86b0-6c26f0c6cb70.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6826
Vulnerability Data
In version 3.25.0 of aimhubio/aim, a denial of service vulnerability exists. By tracking a large number of `Text` objects and then querying them simultaneously through the web API, the Aim web server becomes unresponsive to other requests for an extended…
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period while processing and returning these objects. This vulnerability can be exploited repeatedly, leading to a complete denial of service.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V4.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover inefficient large-table queries during development or updates.
Resource allocation limits the availability impact of expensive joins/sub-queries without stopping the inefficient code.
System monitoring can observe and flag excessive query volume or duration after the weakness is exercised.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent inefficient query patterns through reviews and testing.
Runtime monitoring of data and software can detect excessive query operations as adverse events.
Vulnerability identification processes can surface this weakness during assessments.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development can detect performance issues caused by excessive query operations.
Secure development lifecycle requires design and code reviews that can catch excessive joins before deployment.
Application security requirements can mandate query-performance and data-volume constraints.
Secure architecture principles include database design and indexing standards that mitigate large-table query risks.
Secure coding rules directly prohibit or limit complex joins and sub-queries on large tables.
Capacity management identifies and mitigates resource exhaustion from inefficient large-table queries.