CVE-2026-75411 in JeecgBoot
Summary
by MITRE • 08/27/2026
JeecgBoot v3.9.2 is vulnerable to Remote command execution. The CodeNode component of the AI Flow module supports Groovy script execution. While the `SecurityCheck` class employs a blacklist mechanism to intercept dangerous calls, the dynamic nature of Groovy allows this blacklist to be completely bypassed through string concatenation and reflection.
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Analysis
by VulDB Data Team • 08/27/2026
The vulnerability identified in JeecgBoot version 3.9.2 represents a critical Remote Code Execution flaw located within the AI Flow module's CodeNode component. This specific feature is designed to facilitate the execution of Groovy scripts, providing users with dynamic scripting capabilities for workflow automation and data processing tasks. The core technical deficiency lies in the implementation of the SecurityCheck class, which attempts to mitigate risks by employing a blacklist mechanism intended to intercept dangerous method calls or object instantiations commonly associated with remote code exploitation. However, this defensive measure is fundamentally flawed due to its static nature and inability to account for the dynamic execution features inherent to the Groovy language environment.
The technical flaw stems from the fact that Groovy supports advanced metaprogramming techniques such as reflection and string concatenation at runtime. Attackers can exploit these capabilities to construct malicious payloads dynamically, effectively bypassing the predefined blacklist rules. By manipulating strings or utilizing reflective calls to access underlying Java classes and methods not explicitly listed in the security filter, an attacker can instantiate arbitrary objects and invoke sensitive system functions. This evasion technique renders the static signature-based detection ineffective, as the actual dangerous operations are assembled at execution time rather than appearing directly in the source code of the submitted script.
From a risk perspective, this vulnerability allows for complete remote command execution with the privileges of the application server process. An authenticated user or an attacker who can inject scripts into the AI Flow module can execute arbitrary system commands, read sensitive configuration files, exfiltrate database credentials, and potentially pivot to other systems within the internal network. The impact is severe because it compromises the integrity, confidentiality, and availability of the entire backend infrastructure supporting the JeecgBoot application. This aligns with CWE-94 Improper Control of Generation of Code (Code Injection) as well as CWE-78 OS Command Injection, highlighting the failure to properly sanitize or restrict dynamic code generation mechanisms.
In terms of threat modeling, this vulnerability maps directly to MITRE ATT&CK technique T1059 Command and Scripting Interpreter, specifically under sub-techniques involving Groovy scripting. The exploitation path typically involves Initial Access through authenticated web interface interaction followed by Execution via the vulnerable script engine. Defense in Depth strategies are essential here since perimeter controls alone cannot prevent this type of application-layer logic flaw. Organizations must treat dynamic code execution features with extreme caution and assume that any blacklist-based filtering is insufficient without additional runtime protection mechanisms such as sandboxing or strict allow-listing of permitted classes and methods.
To mitigate this vulnerability, immediate remediation should focus on upgrading to a patched version of JeecgBoot where the security model has been revised to support more robust input validation and execution restrictions. If an upgrade is not immediately feasible, administrators should disable the CodeNode component or restrict access to it strictly for trusted internal users with elevated privileges only. Implementing Web Application Firewall rules that detect patterns associated with Groovy reflection attacks can provide a temporary layer of defense, although this is not a substitute for fixing the underlying code logic. Long-term solutions involve refactoring the script execution engine to use an allow-list approach rather than a blacklist, ensuring that only explicitly approved classes and methods are accessible during runtime evaluation.