A newly disclosed vulnerability in LangChainGo, the Go implementation of the popular LLM orchestration framework LangChain, has raised significant security concerns. Tracked as CVE-2025-9556, this flaw enables unauthenticated attackers to perform arbitrary file reads on servers by injecting malicious prompt templates.
How the Attack Works
LangChainGo supports the use of Jinja2 syntax when parsing prompts, which is processed using the Gonja library v1.5.3. Gonja’s support for the include and extends directives allows attackers to inject statements into prompts that can read sensitive files, such as /etc/passwd. This leads to a server-side template injection (SSTI) vulnerability, allowing attackers to access critical system files without requiring direct system access.
Potential Impact
The consequences of this vulnerability are severe and include:
- Unauthorized access to sensitive files, including configuration files and user credentials.
- Exposure of critical system information that can aid in further attacks.
- Increased risk of privilege escalation and lateral movement within networks.
- Potential compromise of systems running LangChainGo, affecting various applications and services.
Mitigation Measures
Organizations using LangChainGo should take immediate action to mitigate this vulnerability:
- Disable template parsing for any untrusted prompt content to prevent malicious injections.
- Restrict file system permissions for processes running LangChainGo to limit access to sensitive files.
- Apply vendor patches and monitor updates from LangChainGo maintainers to ensure timely remediation.
- Implement monitoring to detect and respond to suspicious file read operations.
- Regularly audit AI-driven applications for prompt injection vulnerabilities and other security risks.
Conclusion
The LangChainGo vulnerability underscores the importance of treating prompts as untrusted input, similar to web forms or API requests. Features designed to enhance flexibility, such as template engines, can inadvertently introduce security risks if not properly controlled. Organizations must adopt a proactive approach to secure AI systems by implementing robust input validation, restricting access to sensitive resources, and staying informed about emerging vulnerabilities.
About COE Security
COE Security partners with organizations across various sectors, including finance, healthcare, legal services, and software development, to strengthen their cybersecurity posture and ensure compliance with data protection regulations. Our services include:
- Incident response to contain and remediate AI-driven security events.
- Architecture reviews to secure large language model integrations and template usage.
- Implementation of least privilege controls to limit access to sensitive resources.
- Ongoing monitoring to detect and block prompt injection attacks.
- Compliance support to meet data protection regulations across sectors.
At COE Security, we help organizations proactively secure their AI systems against emerging threats and maintain a resilient cybersecurity posture.