CVE-2024-8309 (GCVE-0-2024-8309)

Vulnerability from cvelistv5 – Published: 2024-10-29 12:50 – Updated: 2025-10-15 12:50
VLAI
Title
SQL Injection in langchain-ai/langchain
Summary
A vulnerability in the GraphCypherQAChain class of langchain-ai/langchain version 0.2.5 allows for SQL injection through prompt injection. This vulnerability can lead to unauthorized data manipulation, data exfiltration, denial of service (DoS) by deleting all data, 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.
Severity
4.9 (Medium)
CVSS:3.0/AV:L/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:L
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-29 13:50 UTC
CWE
  • CWE-89 - Improper Neutralization of Special Elements used in an SQL Command
Assigner
References
Impacted products
Vendor Product Version
langchain-ai langchain-ai/langchain Affected: unspecified , < 0.3.0 (custom)
Create a notification for this product.
langchain-ai langchain Affected: 0 , < 0.3.0 (custom)
    cpe:2.3:a:langchain-ai:langchain:*:*:*:*:*:*:*:*
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.


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Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.