A weakness has been identified in langflow-ai langflow up to version 1.8.4. This vulnerability allows code injection via the eval function found in the file src/lfx/src/lfx/components/llm_operations/lambda_filter.p of the component LambdaFilterComponent. The attack may be performed from remote, and the exploit has been made available to the public. The vendor was contacted early about this disclosure but did not respond in any way.
The CVSS score for this vulnerability is 2.1, classifying it as low severity. Although the score indicates lower urgency compared to higher-severity vulnerabilities, organizations should still be aware of the risks associated with this issue. The potential for exploitation exists, and defending against this vulnerability is essential.
Risk to organizations includes the possibility of unauthorized remote code execution, which could lead to further compromises of the affected systems. Although the exploit complexity is low, the requirement for low privileges reduces the barriers for potential attackers.
Organizations should prioritize patching immediately to mitigate risks associated with this vulnerability. Continuous monitoring and implementing security best practices can help in identifying potential exploitation attempts.
Vulnerability Details
The vulnerability is classified under CWE-74 (Injection) and CWE-94 (Code Injection). The CVSS score of 2.1 indicates that while the vulnerability is not critical, it still presents risks that should not be overlooked. The affected component is langflow-ai langflow, particularly versions up to 1.8.4.
Technical Analysis
The root cause of this vulnerability stems from insufficient validation in the eval function of the LambdaFilterComponent. This function's design allows for user-provided input to be executed without adequate sanitization, leading to code injection risks. Attackers may leverage this weakness to execute arbitrary code on affected systems.
The attack vector is network-based, allowing remote attackers to exploit this vulnerability. The attack complexity is low, with no user interaction required, making it accessible for exploitation. The confidentiality, integrity, and availability impacts are all classified as low.
Risk & Impact Analysis
Organizations utilizing langflow-ai langflow up to version 1.8.4 should assess their exposure to this vulnerability. The potential risk includes unauthorized code execution leading to system compromise. The low CVSS score reflects a lower immediate threat, but the existence of a public exploit increases the urgency for remediation.
Given that the vulnerability has been made public, organizations must be proactive in their security measures. Continuous monitoring of network traffic and application behavior can aid in detecting potential exploitation attempts. The urgency for addressing this vulnerability should be reflected in the organization's patch management cycle.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | Yes |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerability affects langflow-ai langflow up to version 1.8.4. Organizations using this version or earlier should take immediate steps to mitigate the risks associated with this vulnerability.
Mitigation & Remediation
Organizations should prioritize patching immediately. Updating to the latest version of langflow-ai langflow that addresses this vulnerability is essential. If a patch is not available, organizations must implement workarounds to limit exposure to this vulnerability.
Security testing can help identify any weaknesses in the current implementation and validate that appropriate security measures are in place.
Detection Guidance
Monitoring network traffic for unusual activity and logging any access attempts to the vulnerable components can help in early detection of exploitation attempts. Behavioral anomalies in application performance may also indicate a potential exploitation.
AppSecure Threat Intelligence Insight
The emergence of this vulnerability illustrates ongoing security challenges in application development, particularly related to input validation. Security teams must remain vigilant against such vulnerabilities and engage in proactive measures to secure applications.
Understanding injection attacks and their root causes is critical for preventing similar vulnerabilities in future software releases.
Penetration testing methodology should be applied to continuously evaluate the security posture of applications.
A robust vulnerability management program can assist organizations in prioritizing and addressing security issues effectively.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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