Incident Score: Analysis & Impact (LAN1774635883)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of LangChain's Vulnerability and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts LangChain Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the LangChain breach identified under incident ID LAN1774635883.
The analysis begins with a detailed overview of LangChain's information like the linkedin page: https://www.linkedin.com/company/langchain, the number of followers: 477971, the industry type: Technology, Information and Internet and the number of employees: 187 employees
After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 774 and after the incident was 757 with a difference of -17 which is could be a good indicator of the severity and impact of the incident.
In the next step of the video, we will analyze in more details the incident and the impact it had on LangChain and their customers.
LangChain recently reported "LangChain and LangGraph Patch Critical Vulnerabilities Exposing Sensitive Data", a noteworthy cybersecurity incident.
LangChain and LangGraph, two widely used open-source frameworks for building AI applications, recently addressed three high-severity vulnerabilities that could allow threat actors to exfiltrate sensitive data.
The disruption is felt across the environment, affecting LangChain and LangGraph, and exposing Filesystem files, Environment secrets and Conversation histories.
In response, moved swiftly to contain the threat with measures like Patches released for vulnerabilities, and began remediation that includes Upgrade to LangChain-core ≥1.2.22, ≥0.3.81, or ≥1.2.5 and Upgrade to LangGraph-checkpoint-sqlite ≥3.0.1.
The case underscores how teams are taking away lessons such as These vulnerabilities highlight broader risks in AI infrastructure, particularly in foundational components like LangChain, which serves as a dependency for hundreds of downstream libraries. The flaws could propagate through the AI stack, affecting integrations and wrappers that inherit the vulnerable code, and recommending next steps like Audit configurations, Avoid enabling `secrets_from_env=True` when deserializing untrusted data and Treat LLM outputs as untrusted input.
Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.
The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with high confidence (90%), supported by evidence indicating exploitation of unpatched vulnerabilities in LangChain/LangGraph frameworks. Under the Credential Access tactic, the analysis identified Cloud Instance Metadata API (T1552.005) with moderate to high confidence (80%), supported by evidence indicating cVE-2025-68664 such as deserialization issue leaking API keys and environment secrets and Unsecured Credentials (T1552) with moderate to high confidence (80%), supported by evidence indicating environment secrets and API keys exposed via vulnerabilities. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating path traversal flaw (CVE-2026-34070) enabling arbitrary file access and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating sQL injection (CVE-2025-67644) exposing conversation histories and workflow data. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating vulnerabilities could allow threat actors to exfiltrate sensitive data and Automated Exfiltration (T1020) with moderate to high confidence (70%), supported by evidence indicating data exfiltration via exploitation of AI framework vulnerabilities. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (70%), supported by evidence indicating prompt injection attacks and exposure of sensitive workflow data and Account Access Removal (T1531) with moderate confidence (50%), supported by evidence indicating potential unauthorized access to Docker configurations and API keys. Under the Defense Evasion tactic, the analysis identified Exploitation for Defense Evasion (T1211) with moderate to high confidence (80%), supported by evidence indicating exploitation of deserialization (CVE-2025-68664) to bypass security controls and Exploit Public-Facing Application (T1190) with moderate to high confidence (70%), supported by evidence indicating vulnerabilities in widely used frameworks enabling evasion of defenses. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- LangChain Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/langchain/incident/LAN1774635883
- LangChain CyberSecurity Rating page: https://www.rankiteo.com/company/langchain
- LangChain Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/lan1774635883-langchain-langgraph-vulnerability-january-2025/
- LangChain CyberSecurity Score History: https://www.rankiteo.com/company/langchain/history
- LangChain CyberSecurity Incident Source: https://www.techradar.com/pro/security/each-vulnerability-exposes-a-different-class-of-enterprise-data-langchain-framework-hit-by-several-worrying-security-issues-heres-what-we-know
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf