CVE-2025-30358: Mesop: class pollution enables DoS and LLM jailbreak

GHSA-f3mf-hm6v-jfhh HIGH
Published March 27, 2025
CISO Take

If your teams use Mesop to build internal LLM chatbots or AI interfaces, patch to 0.14.1 now — this requires only low privileges over the network. Beyond the straightforward DoS, the AI-specific risk is serious: an attacker can overwrite conversation role assignments (user/assistant/system) at runtime, enabling jailbreak attacks against any LLM your Mesop app fronts. This is a textbook example of a classic web vulnerability (prototype/class pollution) creating a novel AI attack surface.

What is the risk?

High risk for teams using Mesop to expose LLM interfaces internally or externally. CVSS 8.1 with network vector, low complexity, low privileges, no user interaction — exploitation is straightforward for any authenticated user. EPSS at 3.1% indicates PoC-level feasibility without confirmed in-the-wild exploitation yet. The DoS path is trivial; the jailbreak path requires understanding of LLM role structures but is accessible to moderately skilled adversaries. Not in CISA KEV but the AI-specific impact warrants elevated urgency for AI/ML teams.

What systems are affected?

Package Ecosystem Vulnerable Range Patched
mesop pip < 0.14.1 0.14.1

Do you use mesop? You're affected.

How severe is it?

CVSS 3.1
8.1 / 10
EPSS
0.6%
chance of exploitation in 30 days
Higher than 43% of all CVEs
Exploitation Status
No known exploitation
Sophistication
Moderate

What is the attack surface?

AV AC PR UI S C I A
AV Network
AC Low
PR Low
UI None
S Unchanged
C None
I High
A High

What should I do?

1 step
  1. 1) Upgrade Mesop to 0.14.1 immediately — patch is available and the fix is straightforward. 2) If patching is blocked, restrict Mesop application access to trusted internal networks and enforce strong authentication ahead of the app layer. 3) Audit all internal AI tools built on Mesop — inventory them via pip freeze or dependency scanning. 4) Review LLM interaction logs for anomalous role assignments or unexpected system prompt overrides as indicators of exploitation. 5) Add Mesop to your SCA/dependency scanning pipeline to catch future vulnerabilities automatically.

What does CISA's SSVC say?

Decision Track
Exploitation none
Automatable No
Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act
Article 15 - Accuracy, robustness and cybersecurity Article 9 - Risk management system
ISO 42001
A.6.1.2 - AI risk assessment A.8.4 - AI system security
NIST AI RMF
MANAGE 2.2 - Mechanisms to sustain the value of AI systems MAP 5.1 - Likelihood and impact of undesired outcomes
OWASP LLM Top 10
LLM01 - Prompt Injection LLM08 - Excessive Agency

Frequently Asked Questions

What is CVE-2025-30358?

If your teams use Mesop to build internal LLM chatbots or AI interfaces, patch to 0.14.1 now — this requires only low privileges over the network. Beyond the straightforward DoS, the AI-specific risk is serious: an attacker can overwrite conversation role assignments (user/assistant/system) at runtime, enabling jailbreak attacks against any LLM your Mesop app fronts. This is a textbook example of a classic web vulnerability (prototype/class pollution) creating a novel AI attack surface.

Is CVE-2025-30358 actively exploited?

No confirmed active exploitation of CVE-2025-30358 has been reported, but organizations should still patch proactively.

How to fix CVE-2025-30358?

1) Upgrade Mesop to 0.14.1 immediately — patch is available and the fix is straightforward. 2) If patching is blocked, restrict Mesop application access to trusted internal networks and enforce strong authentication ahead of the app layer. 3) Audit all internal AI tools built on Mesop — inventory them via pip freeze or dependency scanning. 4) Review LLM interaction logs for anomalous role assignments or unexpected system prompt overrides as indicators of exploitation. 5) Add Mesop to your SCA/dependency scanning pipeline to catch future vulnerabilities automatically.

What systems are affected by CVE-2025-30358?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, model serving, AI chatbot interfaces, RAG pipelines.

What is the CVSS score for CVE-2025-30358?

CVE-2025-30358 has a CVSS v3.1 base score of 8.1 (HIGH). The EPSS exploitation probability is 0.59%.

What is the AI security impact?

Affected AI Architectures

agent frameworksmodel servingAI chatbot interfacesRAG pipelines

MITRE ATLAS Techniques

AML.T0029 Denial of AI Service
AML.T0049 Exploit Public-Facing Application
AML.T0051 LLM Prompt Injection
AML.T0054 LLM Jailbreak
AML.T0073 Impersonation
AML.T0080 AI Agent Context Poisoning

Compliance Controls Affected

EU AI Act: Article 15, Article 9
ISO 42001: A.6.1.2, A.8.4
NIST AI RMF: MANAGE 2.2, MAP 5.1
OWASP LLM Top 10: LLM01, LLM08

What are the technical details?

Original Advisory

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 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.

Exploitation Scenario

An adversary with a low-privilege account on a Mesop-based internal LLM chat tool crafts a malicious HTTP request that exploits the class pollution vulnerability. By overwriting Mesop's global conversation state, they inject a forged 'system' role message containing instructions to ignore all previous safety guidelines. The LLM, receiving what it interprets as a legitimate system-level directive, complies — effectively jailbroken without any prompt injection against the LLM itself. The attacker then exfiltrates sensitive data the LLM has access to via RAG or tool calls, or escalates to DoS by corrupting critical runtime state and crashing the server.

Weaknesses (CWE)

CWE-915 — Improperly Controlled Modification of Dynamically-Determined Object Attributes: The product receives input from an upstream component that specifies multiple attributes, properties, or fields that are to be initialized or updated in an object, but it does not properly control which attributes can be modified.

  • [Implementation] If available, use features of the language or framework that allow specification of allowlists of attributes or fields that are allowed to be modified. If possible, prefer allowlists over denylists. For applications written with Ruby on Rails, use the attr_accessible (allowlist) or attr_protected (denylist) macros in each class that may be used in mass assignment.
  • [Architecture and Design, Implementation] 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.

Source: MITRE CWE corpus.

CVSS Vector

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H

Timeline

Published
March 27, 2025
Last Modified
March 27, 2025
First Seen
March 27, 2025

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