Cyber Resilience

CVE-2024-7042

CriticalPublic PoC

Published: 29 October 2024

Published
29 October 2024
Modified
31 October 2024
KEV Added
—
Patch
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CVSS Score v3.1 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
EPSS Score 0.0006 19.6th percentile
Risk Priority 20 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2024-7042 is a critical-severity SQL Injection (CWE-89) vulnerability in Langchain Langchain. Its CVSS base score is 9.8 (Critical).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 19.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the LLM/Generative AI Risks risk domain; MITRE ATLAS techniques in scope: Direct (AML.T0051.000), Hardware (AML.T0010.000), Financial Harm (AML.T0048.000).

EU & UK References

Vulnerability details

A vulnerability in the GraphCypherQAChain class of langchain-ai/langchainjs versions 0.2.5 and all versions with this class allows for prompt injection, leading to SQL injection. This vulnerability permits unauthorized data manipulation, data exfiltration, denial of service (DoS) by deleting all data,…

more

breaches in multi-tenant security environments, and data integrity issues. Attackers can create, update, or delete nodes and relationships without proper authorization, extract sensitive data, disrupt services, access data across different tenants, and compromise the integrity of the database.

CWE(s)
CWE-89

AI Security AnalysisAI

AI Category
AI Agent Protocols and Integrations
Risk Domain
LLM/Generative AI Risks
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
The vulnerability is in GraphCypherQAChain of langchain-ai/langchainjs, a LangChain component for integrating LLMs with graph databases (e.g., Neo4j via Cypher queries generated from natural language prompts). LangChain is a framework for building AI agents and LLM chains with tool integrations, fitting 'AI Agent Protocols and Integrations'. Prompt injection is a core AI/LLM risk confirmed by the AI/ML bug bounty context.

Related Threats

MITRE ATT&CK Enterprise TechniquesAI

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
attack.mitre.org →
T1213.006 Databases Collection
Adversaries may leverage databases to mine valuable information.
attack.mitre.org →
T1485 Data Destruction Impact
Adversaries may destroy data and files on specific systems or in large numbers on a network to interrupt availability to systems, services, and network resources.
attack.mitre.org →
T1565.001 Stored Data Manipulation Impact
Adversaries may insert, delete, or manipulate data at rest in order to influence external outcomes or hide activity, thus threatening the integrity of the data.
attack.mitre.org →
Why these techniques?

Prompt injection vulnerability enables exploitation of public-facing applications (T1190) via arbitrary Cypher queries for database data collection (T1213.006), stored data manipulation including create/update/delete (T1565.001), and data destruction for DoS (T1485).

MITRE ATLAS TechniquesAI

MITRE ATLAS techniques

AML.T0051.000: DirectAML.T0010.000: HardwareAML.T0048.000: Financial Harm

Affected Assets

langchain
langchain
≤ 0.3.1

Mitigating Controls

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

CA-8 Penetration Testing
addresses: CWE-89

Penetration testing uses SQL injection payloads against database interfaces, identifying and supporting fixes for SQL injection weaknesses.

SI-10 Information Input Validation
addresses: CWE-89

Validates query inputs to prevent SQL syntax or command manipulation.

References