Critical LangChainGo Vulnerability Enables Sensitive File Access via Malicious Prompts

A severe vulnerability in LangChainGo, the Go implementation of the LangChain orchestration framework for large language models, has been publicly disclosed.

Tracked as CVE-2025-9556, the flaw permits unauthenticated attackers to execute arbitrary file reads on servers by injecting malicious prompt templates.

The vulnerability was first made public on 2025-09-12 and risks exposing critical data such as configuration files, private keys, and user credentials, potentially enabling privilege escalation or lateral network movement.

Details of the Vulnerability

LangChainGo leverages Gonja, a Go-based implementation of Jinja2, to parse and render dynamic prompt templates.

Under normal conditions, Gonja’s support for directives like {% include %}, {% from %}, and {% extends %} facilitates modular template design.

However, when user-controlled input is passed directly into the template engine, an attacker can craft templates that reference arbitrary filesystem paths.

For example, a malicious template string such as {% include "/etc/passwd" %} will cause Gonja to load and disclose the contents of /etc/passwd.

This server-side template injection (SSTI) bypasses conventional input validation and leads to unauthorized file reads.

The vulnerability arises from the absence of filesystem access restrictions in the default rendering functions.

By exploiting this weakness, an attacker with only prompt input privileges can fetch any file readable by the application’s user account.

In LLM-powered chatbot interfaces or automated document-generation services using LangChainGo, no additional authentication or privilege escalation is required to trigger the exploit.

Impact and Exploitation Scenarios

The arbitrary file read issue undermines the confidentiality guarantees of systems running LangChainGo.

Sensitive files such as SSH private keys, database credentials, TLS certificates, and configuration files may be exposed in plaintext.

Once obtained, these artifacts can be weaponized to escalate privileges, pivot within internal networks, or maintain persistent backdoor access.

A report by KBCert highlights real-world deployment scenarios where chatbots process untrusted user inputs without adequate sanitization, making them prime targets for this exploit.

In multi-tenant environments or shared hosting platforms, a successful SSTI attack could compromise multiple applications or even entire host systems.

Organizations using LangChainGo in production are advised to assume that any exposed prompt or template parameter may be exploited.

Mitigation and Patching

The maintainers of LangChainGo have responded by releasing a security update that introduces the RenderTemplateFS function.

This new API isolates template rendering from the filesystem by default, only allowing inclusion of approved files through a controlled virtual filesystem interface.

Unauthorized file access attempts are now blocked at the template engine layer.

After upgrading, developers must review existing custom prompt templates to ensure they invoke RenderTemplateFS instead of legacy rendering functions.

It is also recommended to audit all user-supplied template inputs, implement strict allow-listing of template filenames, and enforce least-privilege access for the application’s operating system user.

Regular dependency audits and adherence to secure coding practices will further reduce the risk of similar vulnerabilities in the future.

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