Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessEnable output redaction in target app
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DeliveryStream LLM prompt through SDK
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ExploitSDK generates unredacted new_token events
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ExecutionAccess LangSmith workspace or intercept SDK traffic
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PersistExtract run events array
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ImpactRecover sensitive token data
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A01:2025 - Broken Access Control
ASVS 5.0
V14 - Data Protection Data exposure prevention
MITRE ATT&CK
2 techniques across 2 tactics: Collection, Discovery The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | Output redaction controls must be explicitly enabled (hideOutputs in JavaScript, hide_outputs in Python) for the vulnerability to manifest as a security failure. The application must use streaming LLM integrations (e.g., streaming token callbacks, token-by-token iteration) that generate new_token events, as non-streaming LLM calls do not populate the events array and thus are unaffected. Exploitation further requires the attacker to have access to run event data within the LangSmith workspace (authenticated access, workspace membership) or ability to intercept unencrypted or improperly validated API traffic between the SDK and LangSmith platform. Notably, applications that do not use the output redaction feature, do not use streaming, or are already storing sensitive data outside LangSmith (i.e., using LangSmith only for non-sensitive operational logging) are not vulnerable. The vulnerability does not affect confidentiality of inputs, only outputs/token streams, and it does not cause data loss or unauthorized modification. |
| Impact | CVSS 5.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N) indicates network-accessible confidentiality impact with low attack complexity and no authentication barrier, yet the real-world risk is contextual. Exploitation requires that: (1) an application explicitly enables output redaction because it handles sensitive data (e.g., PII, secrets, medical records), (2) the application uses streaming LLM calls, and (3) an attacker can access the LangSmith workspace or intercepted API traffic. The low CVSS score reflects limited scope (confidentiality only, no integrity or availability impact) and the requirement that the attacker must already have some visibility into run events-this is not a vulnerability that exposes data to arbitrary internet actors without authentication to LangSmith itself. However, for organizations using LangSmith as a secure logging backend for sensitive workloads, this represents a critical logic bypass: the redaction feature they depend on silently fails for streaming output. EPSS score not provided, but exploitation probability is moderate to high within affected deployments since attackers need only access to the LangSmith platform (via compromised credentials, insider threat, or shared workspace) or network interception of SDK-to-platform traffic. No CISA KEV status provided; patch is available, indicating this is a vendor-confirmed logic defect rather than an exploited zero-day. Organizations that do not use output redaction or do not use streaming integrations are unaffected. |
| Exploit ScenarioDerived | An attacker with legitimate or compromised access to a LangSmith workspace (or positioned to intercept SDK-to-platform HTTPS traffic via network compromise) observes that an organization has enabled output redaction on an application handling customer PII. The attacker streams a prompt through the vulnerable LangSmith SDK that causes an LLM to output customer email addresses and phone numbers. Although the application's redaction rules would filter this from the outputs field, the raw tokens appear unredacted in the run's events array as new_token objects. The attacker exports the run data and recovers the complete streamed output, bypassing the intended security control. |
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Alert meAnalysisAIv1 – Apr 23, 2026
LangSmith Client SDKs in JavaScript (prior to 0.5.19) and Python (prior to 0.7.31) fail to apply output redaction controls to streaming token events, allowing sensitive LLM-generated content to leak into LangSmith platform storage despite hideOutputs/hide_outputs being enabled. Unauthenticated remote attackers can intercept or access unredacted streamed tokens if they gain visibility into run events, bypassing the intended confidentiality controls.
RemediationAI
Vendor-released patch: Upgrade to LangSmith JavaScript SDK version 0.5.19 or later, and Python SDK version 0.7.31 or later. Both versions restore redaction coverage to the events array, ensuring new_token events are processed through the redaction pipeline. Organizations should apply these upgrades immediately if they rely on the hideOutputs (JavaScript) or hide_outputs (Python) feature for compliance or data protection. No workarounds are available short of patching - disabling streaming integrations entirely would preserve redaction guarantees but is not practical for production LLM applications. Interim mitigations include restricting LangSmith workspace access via IAM, encrypting network traffic between SDK and LangSmith platform with TLS, and monitoring LangSmith workspace access logs for unexpected data access. See GitHub security advisory GHSA-rr7j-v2q5-chgv for detailed patch notes.
Affected VersionsENISA
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 2 packages in npm, pypi, euvd
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
The LangSmith SDKs implement output redaction through prepareRunCreateOrUpdateInputs (JavaScript) and _hide_run_outputs (Python) functions that sanitize the inputs and outputs fields on run objects before transmission to the LangSmith platform. However, when an LLM generates streaming output (common in modern LLM integrations), each token chunk is recorded as a separate new_token event appended to the events array within the run object. The redaction pipeline processes only the top-level inputs and outputs fields and completely bypasses the events array, causing raw token values to persist unredacted. This is fundamentally an information disclosure flaw (CWE-200) where trust boundaries fail - applications assume redaction applies uniformly across all output channels, but the streaming event path operates outside the redaction mechanism. The vulnerability affects the langsmith-sdk package across its Python and JavaScript implementations.
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Severity by source
AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
Primary rating from Vendor (GitHub_M).
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
3DescriptionCVE.org
LangSmith Client SDKs provide SDK's for interacting with the LangSmith platform. Prior to version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK, the LangSmith SDK's output redaction controls (hideOutputs in JS, hide_outputs in Python) do not apply to streaming token events. When an LLM run produces streaming output, each chunk is recorded as a new_token event containing the raw token value. These events bypass the redaction pipeline entirely - prepareRunCreateOrUpdateInputs (JS) and _hide_run_outputs (Python) only process the inputs and outputs fields on a run, never the events array. As a result, applications relying on output redaction to prevent sensitive LLM output from being stored in LangSmith will still leak the full streamed content via run events. Version 0.5.19 of the JavaScript SDK and version 0.7.31 of the Python SDK fix the issue.
Blast Radius
ecosystem impact- 5 npm packages depend on langsmith (5 direct, 2 indirect)
Ecosystem-wide dependent count for version 0.5.19.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.
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Exploit-DB