CVE-2026-7871 Overview
CVE-2026-7871 is an insecure deserialization vulnerability [CWE-502] affecting IBM Langflow OSS versions 1.0.0 through 1.10.0. The flaw allows users with Redis access to execute arbitrary code with full application privileges. Successful exploitation compromises all secrets, data, and system integrity within the Langflow deployment.
Langflow is an open-source framework for building applications with large language models (LLMs). The vulnerability affects deployments that use Redis as a backing store, which is common in production environments. IBM published a support advisory documenting the issue and providing remediation guidance.
Critical Impact
Attackers with Redis access can achieve arbitrary code execution with full application privileges, exposing secrets, sensitive data, and the entire host environment.
Affected Products
- IBM Langflow OSS 1.0.0
- IBM Langflow OSS versions 1.0.1 through 1.9.x
- IBM Langflow OSS 1.10.0
Discovery Timeline
- 2026-06-30 - CVE-2026-7871 published to NVD
- 2026-07-02 - Last updated in NVD database
Technical Details for CVE-2026-7871
Vulnerability Analysis
CVE-2026-7871 is an insecure deserialization flaw classified under [CWE-502]. Langflow deserializes untrusted data stored in Redis without validating its origin or integrity. When the application reads a poisoned Redis entry, the deserialization routine instantiates attacker-controlled objects and executes their embedded logic.
The attack requires access to the Redis instance backing Langflow. In many deployments, Redis is exposed on internal networks without authentication, exists on shared infrastructure, or accepts connections from adjacent workloads. An attacker who reaches Redis can plant a malicious serialized payload that Langflow later consumes, gaining code execution in the Langflow process context.
Because Langflow typically holds API keys, model credentials, and pipeline definitions, code execution inside the process yields immediate access to those secrets. The attacker inherits the privileges of the Langflow application user on the underlying host.
Root Cause
The root cause is the use of an unsafe deserialization mechanism against data retrieved from Redis. Langflow treats Redis as a trusted data source and does not enforce type restrictions, signature verification, or schema validation on cached objects. Any process that can write to Redis can therefore inject executable object graphs.
Attack Vector
The attack vector is network-based. An attacker connects to the Redis instance, writes a crafted serialized payload to a key that Langflow reads, and waits for Langflow to consume the entry. When Langflow deserializes the payload, arbitrary code runs in the application process. No authentication to Langflow itself is required if Redis access is available.
No verified public proof-of-concept code is available for CVE-2026-7871. Refer to the IBM Support Page for vendor-provided technical details.
Detection Methods for CVE-2026-7871
Indicators of Compromise
- Unexpected child processes spawned by the Langflow application user, particularly shells, Python subprocesses, or network utilities.
- Outbound connections from the Langflow host to unfamiliar external addresses shortly after Redis reads.
- Anomalous Redis keys containing serialized binary blobs, pickle headers, or oversized values in namespaces used by Langflow.
- Modification of Langflow flows, credentials, or environment files without a corresponding administrator action.
Detection Strategies
- Monitor Langflow process behavior for deviations from expected Python execution patterns, focusing on exec, os.system, and subprocess calls.
- Inspect Redis MONITOR or slowlog output for SET operations from unexpected clients writing to Langflow-owned keys.
- Correlate Redis write events with subsequent process creation on Langflow hosts to identify deserialization triggers.
- Alert on Langflow reading Redis keys immediately followed by outbound network activity or credential file access.
Monitoring Recommendations
- Log all Redis client authentications and source IP addresses, then baseline expected clients.
- Enable process-level auditing on Langflow hosts using auditd or equivalent to capture execve events.
- Ingest Langflow application logs and Redis logs into a centralized platform for cross-source correlation.
- Track versions of Langflow across your fleet to identify any host still running 1.0.0 through 1.10.0.
How to Mitigate CVE-2026-7871
Immediate Actions Required
- Upgrade IBM Langflow OSS to a fixed release as documented in the IBM Support Page.
- Restrict Redis network exposure so only the Langflow application host can connect, using firewall rules or network policies.
- Enable Redis authentication with a strong password and, where supported, TLS between Langflow and Redis.
- Rotate all secrets, API keys, and model credentials stored or referenced by Langflow if compromise is suspected.
Patch Information
IBM has published remediation guidance on the IBM Support Page. Administrators should upgrade Langflow beyond version 1.10.0 to the vendor-designated fixed release. Verify the deployed version with pip show langflow or the equivalent package inspection command after upgrading.
Workarounds
- Bind Redis to 127.0.0.1 or a private interface accessible only to the Langflow host until patching is complete.
- Deploy Redis Access Control Lists (ACLs) to limit which commands the Langflow client can invoke.
- Isolate Langflow and its Redis backend in a dedicated network segment with strict ingress and egress filtering.
- Audit existing Redis contents for anomalous serialized payloads before restarting Langflow after upgrade.
# Configuration example: restrict Redis exposure and require authentication
# /etc/redis/redis.conf
bind 127.0.0.1
protected-mode yes
requirepass <strong-random-password>
# Firewall rule limiting Redis access to the Langflow host
iptables -A INPUT -p tcp --dport 6379 -s <langflow-host-ip> -j ACCEPT
iptables -A INPUT -p tcp --dport 6379 -j DROP
# Verify upgraded Langflow version
pip show langflow | grep -i version
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

