LangChain Image Fetch Flaw Enables SSRF Risk (CVE-2026-26013)

Key Takeaways

• SSRF vulnerability discovered in LangChain image URL handling
• Flaw affected token counting logic in vision-enabled workflows
• Could allow access to internal services or metadata endpoints
• Highlights AI framework supply chain exposure

AI Framework Token Logic Introduces SSRF Exposure

In February 2026, CVE-2026-26013 disclosed a server-side request forgery vulnerability in LangChain related to image URL handling during token counting for vision-enabled workflows. According to the National Vulnerability Database at https://nvd.nist.gov/vuln/detail/CVE-2026-26013, the flaw allowed unvalidated URLs to be fetched, potentially exposing internal services. The issue reinforces growing risks within AI orchestration frameworks that bridge models, APIs, and external resources.

What We Know

CVE-2026-26013 was published on February 10, 2026. The National Vulnerability Database entry at https://nvd.nist.gov/vuln/detail/CVE-2026-26013 describes the issue as an SSRF vulnerability stemming from insufficient validation of image_url inputs in LangChain’s token counting logic.

The GitHub Advisory Database provides additional context at https://github.com/advisories/GHSA-2g6r-c272-w58r, identifying the affected versions and remediation guidance. Red Hat also published a security advisory referencing the CVE at https://access.redhat.com/security/cve/CVE-2026-26013.

LangChain is a widely used open-source framework for building LLM-powered applications and agents. The vulnerability did not involve model poisoning or prompt injection. Instead, it arose from application-layer logic in the orchestration framework that processed externally supplied URLs without adequate restriction.

At the time of disclosure, there were no confirmed reports of widespread active exploitation. However, SSRF vulnerabilities are commonly leveraged to access cloud metadata services or internal APIs.

What Could Happen

Server-side request forgery occurs when an application fetches a URL supplied by a user without validating or restricting its destination. In this case, LangChain’s token counting mechanism for image-based inputs could fetch arbitrary image_url values.

Because AI orchestration frameworks often operate within cloud environments with access to internal services, metadata endpoints, or private APIs, an SSRF flaw can allow attackers to pivot laterally. For example, malicious inputs could direct the application to request internal IP addresses or cloud instance metadata endpoints.

AI frameworks amplify SSRF risk because they frequently ingest dynamic inputs from users, external APIs, and agent chains. Vision-enabled features add additional pathways for external resource fetching. The combination of autonomous agent behavior and unvalidated network requests increases exposure compared to traditional web applications.

If exploited, this vulnerability could enable credential theft, internal reconnaissance, or access to sensitive configuration data. In AI-driven systems that chain multiple tools and APIs together, such access could cascade across services.

Why It Matters

LangChain is widely embedded in AI-powered applications, including enterprise chatbots, data analysis tools, and autonomous agent systems. An SSRF vulnerability in a core framework introduces systemic risk across downstream applications.

Although no confirmed exploitation was reported at disclosure, SSRF vulnerabilities are well understood attack primitives that frequently lead to cloud credential exposure. Organizations using LangChain in production environments may unknowingly expose internal services if input validation is insufficient.

This incident also reinforces the importance of secure AI orchestration under frameworks such as the NIST AI Risk Management Framework at https://www.nist.gov/itl/ai-risk-management-framework. AI-specific functionality does not eliminate traditional web security risks. Instead, it often increases the complexity and attack surface.

As enterprises scale AI integrations, open-source AI frameworks must be evaluated with the same scrutiny applied to other critical supply chain components.

PointGuard AI Perspective

CVE-2026-26013 highlights the growing intersection between traditional web vulnerabilities and AI-native application design. AI frameworks like LangChain act as orchestration layers, connecting models, APIs, tools, and external resources. Weak validation logic within these layers can expose entire AI ecosystems.

PointGuard AI provides continuous monitoring of AI framework dependencies and integration pathways. Through AI SBOM visibility, organizations gain transparency into open-source AI components embedded in their environments, including frameworks such as LangChain.

Our policy enforcement capabilities help restrict unsafe outbound network calls and flag anomalous data flows between AI services and internal infrastructure. By correlating signals across APIs, model interactions, and network behavior, PointGuard AI can detect patterns consistent with SSRF exploitation attempts.

AI-native security requires visibility beyond model behavior. It must extend into orchestration logic, external resource handling, and agent-driven execution flows. PointGuard AI enables organizations to proactively manage AI framework risk while supporting secure and scalable AI adoption.

Incident Scorecard Details

Total AISSI Score: 7.8/10

Criticality = 7, Exposure of internal services and cloud metadata endpoints, AISSI weighting: 25%

Propagation = 8, Widely used AI framework with potential cross-application impact, AISSI weighting: 20%

Exploitability = 6, Publicly disclosed SSRF vector with well-known exploitation techniques, AISSI weighting: 15%

Supply Chain = 9, Heavy enterprise reliance on open-source AI orchestration framework, AISSI weighting: 15%

Business Impact = 7, Credible potential for credential exposure and service compromise, AISSI weighting: 25%

Sources

National Vulnerability Database – CVE-2026-26013
https://nvd.nist.gov/vuln/detail/CVE-2026-26013

GitHub Advisory Database – GHSA-2g6r-c272-w58r
https://github.com/advisories/GHSA-2g6r-c272-w58r

Red Hat Security Advisory – CVE-2026-26013
https://access.redhat.com/security/cve/CVE-2026-26013