Code InjectionWeakness · CWE-94

CVE-2025-46059

CRITICAL · 9.8 CVSS v3.1 Published 2025-07-29
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
langchain-ai v0.3.51 was discovered to contain an indirect prompt injection vulnerability in the GmailToolkit component. This vulnerability allows attackers to execute arbitrary code and compromise the application via a crafted email message. NOTE: this is disputed by the Supplier because the code-execution issue was introduced by user-written code that does not adhere to the LangChain security practices.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · moderate confidence

This is an indirect prompt injection vulnerability in LangChain's GmailToolkit (v0.3.51) where a maliciously crafted email message can inject arbitrary prompts into the LLM processing pipeline, potentially leading to code execution. The attacker sends specially crafted email content that gets processed by the toolkit and passed to an LLM without proper input sanitization or prompt isolation, allowing the injected content to influence LLM behavior. The supplier disputes this as a vulnerability, claiming the code execution results from user code not following LangChain security practices.

MitigationImplement input validation and sanitization for all email content before it reaches the LLM, use prompt isolation techniques such as separate system prompts or parameterization, and ensure any user-provided code adheres to LangChain's documented security practices for handling external inputs. Consider adding warning documentation about the risks of processing untrusted email content.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

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

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify LangChain GmailToolkit usage
    Search your codebase for imports of langchain.tools.gmail or GmailToolkit, and review any files that handle email content processing
    Affected if GmailToolkit is imported and used in the codebase
  2. Check installed LangChain version
    Run 'pip show langchain' or check your dependency lock file (requirements.txt, pyproject.toml) for the langchain version number
    Affected if Version is 0.3.51 or falls within the vulnerable range around this version
  3. Trace how email content (subject, body, sender) flows from GmailToolkit to your LLM chain; look for the chain or agent invocation that processes email data
    Affected if Email content is passed directly to an LLM without intermediate processing
  4. Inspect input handling on email data
    Search for any sanitization, validation, or prompt isolation logic applied to email content before LLM invocation (e.g., input validators, prompt templates with isolation)
    Affected if No sanitization or prompt isolation is applied to raw email content before LLM processing
  5. Review prompt construction method
    Examine how prompts are built when email data is included; look for whether email content is placed in user messages without isolation from system prompts
    Affected if Email content is injected into prompts without proper separation from system instructions

You are affected if your application uses LangChain GmailToolkit (around v0.3.51) and processes email content through an LLM without sanitizing or isolating the email input from the prompt structure.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Implement input validation and sanitization for all email content before it reaches the LLM, use prompt isolation techniques such as separate system prompts or parameterization, and ensure any user-provided code adheres to LangChain's documented security practices for handling external inputs. Consider adding warning documentation about the risks of processing untrusted email content.

Have this fixed Scoped from the published advisory
  • Consultation4.0 h
  • Implementation12.0 h
  • Testing8.0 h
  • Review / QA4.0 h
28.0 hours of engineering $4,880
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2025-46059 in production — separate from our analysis above.

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data