Summary
DB-GPT has a code injection flaw in the CodeAction component's execute_code function in code_utils.py. If attacker-controlled input can reach this function, a remote attacker may cause code to be executed with the privileges of the DB-GPT process.
DB-GPT has a code injection flaw in the CodeAction component's execute_code function in code_utils.py. If attacker-controlled input can reach this function, a remote attacker may cause code to be executed with the privileges of the DB-GPT process.
Affected context
The issue affects the CodeAction component in eosphoros-ai DB-GPT, in the execute_code function in code_utils.py. It is relevant where the function is reachable through a remote-facing workflow and processes input that an attacker can influence. The description does not specify whether authentication is required or what deployment settings expose the affected path.
The issue affects the CodeAction component in eosphoros-ai DB-GPT, in the execute_code function in code_utils.py. It is relevant where the function is reachable through a remote-facing workflow and processes input that an attacker can influence. The description does not specify whether authentication is required or what deployment settings expose the affected path.
Exploit preconditions
- Attack surface: A remotely reachable workflow that invokes CodeAction's
execute_codefunction - Required access: The description does not establish whether authentication or a particular account is required
- Trigger point: Attacker-influenced input is processed by the affected function
- Why exploitation is possible: Input may be handled in a way that permits code injection
- Attack surface: A remotely reachable workflow that invokes CodeAction's
execute_codefunction - Required access: The description does not establish whether authentication or a particular account is required
- Trigger point: Attacker-influenced input is processed by the affected function
- Why exploitation is possible: Input may be handled in a way that permits code injection
Why this happens
The flaw is categorized as CWE-94: Improper Control of Generation of Code ('Code Injection'), with CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component also associated. At a high level, the vulnerable pattern is allowing input to influence code that is later interpreted or executed without adequate separation between data and executable instructions.
The flaw is categorized as CWE-94: Improper Control of Generation of Code ('Code Injection'), with CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component also associated. At a high level, the vulnerable pattern is allowing input to influence code that is later interpreted or executed without adequate separation between data and executable instructions.
Potential impact
- An attacker may execute code with the privileges of the DB-GPT process.
- The application or its accessible data could be compromised.
- Depending on deployment permissions and network access, an attacker could potentially affect connected services or systems.
- An attacker may execute code with the privileges of the DB-GPT process.
- The application or its accessible data could be compromised.
- Depending on deployment permissions and network access, an attacker could potentially affect connected services or systems.
General mitigation guidance
Defensive patterns focus on preventing untrusted input from becoming executable code:
- Prefer fixed, allowlisted operations over dynamically generated or evaluated code.
- Keep user-controlled values as data, using structured interfaces and safe parameterization.
- Apply strict validation at trust boundaries and avoid relying on filtering alone.
- Restrict access to workflows that invoke code execution, and run the application with least privilege.
- Isolate execution environments and limit their filesystem, network, and credential access where feasible.
Defensive patterns focus on preventing untrusted input from becoming executable code:
- Prefer fixed, allowlisted operations over dynamically generated or evaluated code.
- Keep user-controlled values as data, using structured interfaces and safe parameterization.
- Apply strict validation at trust boundaries and avoid relying on filtering alone.
- Restrict access to workflows that invoke code execution, and run the application with least privilege.
- Isolate execution environments and limit their filesystem, network, and credential access where feasible.
How teams detect issues like this
- Use static analysis to identify dynamic evaluation, shell invocation, or code-generation paths that consume external input.
- Review CodeAction call chains to establish whether remote inputs can reach
execute_codeand whether authentication or authorization checks apply. - Use code review checks for unsafe handling of input at interpreter or execution boundaries.
- Enforce policies that restrict dynamic code execution and require security review for changes to these paths.
- Monitor application and runtime events for unexpected execution behavior, while avoiding assumptions that logs alone will identify every attempt.
- Use static analysis to identify dynamic evaluation, shell invocation, or code-generation paths that consume external input.
- Review CodeAction call chains to establish whether remote inputs can reach
execute_codeand whether authentication or authorization checks apply. - Use code review checks for unsafe handling of input at interpreter or execution boundaries.
- Enforce policies that restrict dynamic code execution and require security review for changes to these paths.
- Monitor application and runtime events for unexpected execution behavior, while avoiding assumptions that logs alone will identify every attempt.
Sources
| Source | Link |
|---|---|
| github. | github.com/ |
| github. | github.com/ |
| github. | github.com/ |
| vuldb. | vuldb.com/ |
| vuldb. | vuldb.com/ |
| vuldb. | vuldb.com/ |
| vuldb. | vuldb.com/ |
