CVE-2026-75062 in langfun
Summary
by MITRE • 08/26/2026
Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection') in the default lf.query Python protocol in Google langfun versions prior to 0.1.2 allows remote unauthenticated attackers to execute arbitrary Python code in the context of the host application via crafted prompt inputs that cause the model to generate executable Python expressions evaluated without a sandbox.
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Analysis
by VulDB Data Team • 08/26/2026
The vulnerability identified as Improper Neutralization of Directives in Dynamically Evaluated Code, commonly referred to as Eval Injection, represents a critical security flaw within Google langfun versions prior to 0.1.2. This issue specifically affects the default lf.query Python protocol used by the framework. The core technical deficiency lies in how the application processes inputs generated by large language models and subsequently evaluates them using Python's built-in eval function without adequate sanitization or sandboxing mechanisms. When a user provides a crafted prompt input, they can manipulate the model to generate executable Python expressions that are then directly executed within the host environment. Because this evaluation occurs outside of any secure execution context such as a restricted namespace or isolated container, the attacker gains the ability to run arbitrary code with the same privileges as the underlying application process.
From an operational perspective, this vulnerability allows remote unauthenticated attackers to achieve full control over the affected system. Since there is no authentication required to trigger the flaw, any external actor interacting with the service can exploit it. The impact of executing arbitrary Python code in this context is severe and multifaceted. Attackers can read sensitive files stored on the host machine, exfiltrate data from memory or databases connected to the application, modify system configurations, install backdoors for persistent access, or launch further attacks against other systems within the network topology accessible by the compromised host. The lack of a sandbox means that standard Python security features like restricted modes are bypassed entirely, leaving the entire operating system surface area exposed to exploitation through simple text-based inputs.
This vulnerability aligns with CWE-95, which describes Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection'). It also maps closely to MITRE ATT&CK technique T1059, specifically sub-technique 006 for Command and Scripting Interpreter: Python. The attack vector relies on the attacker's ability to influence the output of a generative model, which is then blindly trusted by the application logic. This pattern highlights a dangerous anti-pattern in software development where dynamic code execution is used without rigorous input validation or isolation boundaries. In modern AI-integrated applications, this risk is amplified because language models are probabilistic and can be coerced into generating malicious payloads through prompt engineering techniques that bypass standard safety filters if those filters rely solely on keyword matching rather than semantic analysis of intent.
To mitigate this vulnerability, developers must immediately upgrade to Google langfun version 0.1.2 or later where the issue has been addressed. For systems that cannot be upgraded instantly, several defensive measures should be implemented. First and foremost, dynamic code execution using eval or exec functions should be avoided entirely in favor of safer alternatives such as parsing structured data formats like JSON for configuration or logic parameters. If dynamic evaluation is absolutely necessary due to legacy constraints, it must be performed within a highly restricted sandbox environment that limits file system access, network connectivity, and process creation capabilities. Additionally, implementing strict input validation on all user-supplied prompts can help reduce the risk of successful exploitation by filtering out known malicious patterns or enforcing allow-lists for expected inputs. Monitoring logs for unusual execution traces related to Python evaluation functions can also aid in early detection of attempted exploits.