CVE-2025-30358
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-30358 is a high-severity Improperly Controlled Modification of Dynamically-Determined Object Attributes (CWE-915) vulnerability. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 50% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-6 (Least Privilege) — 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.
Mesop, a Python-based UI framework for building web applications, is affected by a class pollution vulnerability in versions prior to 0.14.1. The flaw, tracked as CWE-915, permits attackers to overwrite global variables and class attributes in specific Mesop modules at runtime, analogous to JavaScript prototype pollution and capable of altering intended data or control flows.
An authenticated attacker with network access can exploit the issue to trigger a denial of service against the server. Depending on application implementation, the same mechanism can enable identity confusion that impersonates assistant or system roles in LLM conversations, facilitating jailbreak attacks, or potentially escalate to remote code execution when suitable gadgets are present.
The associated GitHub Security Advisory and patch commit indicate that upgrading to Mesop 0.14.1 fully resolves the vulnerability.
The EPSS score rose from a low starting value of 0.0236 to a peak of 0.0369, indicating emerging exploitation interest after disclosure. The framework's use in LLM-facing interfaces adds particular relevance for AI/ML deployments.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-14804
Vulnerability Data
Mesop is a Python-based UI framework that allows users to build web applications. A class pollution vulnerability in Mesop prior to version 0.14.1 allows attackers to overwrite global variables and class attributes in certain Mesop modules during runtime. This vulnerability…
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could directly lead to a denial of service (DoS) attack against the server. Additionally, it could also result in other severe consequences given the application's implementation, such as identity confusion, where an attacker could impersonate an assistant or system role within conversations. This impersonation could potentially enable jailbreak attacks when interacting with large language models (LLMs). Just like the Javascript's prototype pollution, this vulnerability could leave a way for attackers to manipulate the intended data-flow or control-flow of the application at runtime and lead to severe consequences like remote code execution when gadgets are available. Users should upgrade to version 0.14.1 to obtain a fix for the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: jailbreak, llms
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Enforces authorizations so that only permitted attributes may be modified on an object.
Limits the set of modifiable attributes a subject is authorized to touch.
Validates incoming attribute names and values so that only explicitly allowed fields are accepted for update.
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 require allow-listing of mutable object attributes and input validation to block mass-assignment flaws.
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.
Secure coding standards explicitly forbid unsafe dynamic attribute assignment and require property allow-lists.
Security testing can detect mass-assignment flaws but does not itself prevent them at runtime.
Secure development lifecycle requires input validation and object-property whitelisting that directly mitigates mass-assignment risks.
Application security requirements include explicit rules for allowable object attributes and safe deserialization.
Secure architecture principles mandate strict control over dynamic object modification and attribute binding.
Information access restriction limits who can modify objects but does not address which attributes may be changed.