Gemini CLI Vulnerability Enables Stealthy Remote Code Execution
July 31st, 2025
Medium
Our Cyber Threat Intelligence Unit is monitoring a critical vulnerability in Google’s Gemini CLI, an AI-assisted command-line interface developed to support software engineering workflows. Security researchers at Tracebit found that attackers could exploit this flaw to covertly run malicious commands on developers’ systems. The vulnerability results from a combination of prompt injection, inadequate command validation, and misleading user interface design.
This was promptly reported to Google shortly after the initial release of Gemini CLI and has since been fixed. Organizations that utilize AI in developer tools should view this incident as a cautionary example of the risks associated with inadequate validation in machine-assisted workflows. This vulnerability illustrates the potential risk of threat actors running code secretly to exfiltrate data by exploiting AI; creating a serious new threat vector impacting development environments, CI/CD pipelines, and software supply chains.
Technical Details
Attack Type: Remote Code Execution via Prompt Injection
Severity: Medium
Delivery Method: Maliciously crafted context files and README.md documents
The attack exploits Gemini CLI’s run_shell_command tool and its support for “context files" (such as GEMINI.md) to inject malicious prompts. Attackers can embed malicious prompts inside seemingly harmless files, like open-source license texts, which Gemini processes without the user's awareness. These prompts are meant to trick the AI into executing commands controlled by an attacker. When a user approves a seemingly legitimate command (e.g., grep), Gemini adds it to a session-level whitelist.
Due to flawed validation logic, attackers can add extra payloads to the approved command, often separated by semicolons or hidden with whitespace padding. Gemini does not securely parse the entire command string, which allows for covert execution of unauthorized commands. To avoid detection, attackers use terminal manipulation techniques to hide the true nature of the commands they execute, making it easier to exfiltrate environment variables, API tokens, and other sensitive data without immediate suspicion.
Impact
Silent Compromise: Attackers can execute arbitrary shell commands on a developer’s machine without their knowledge, resulting in covert system compromise.
Credential Theft: Environment variables, API keys, and access tokens could be exfiltrated, potentially exposing internal infrastructure and cloud services.
Supply Chain Risk: The integrity of CI/CD pipelines and production deployments is jeopardized, increasing the risk of downstream compromise.
Codebase Contamination: Developers can accidentally introduce or spread malicious code, impacting collaborators and users through shared repositories.
Persistent Access: The attack’s stealthy nature allows adversaries to maintain persistence and gradually escalate privileges without triggering detection mechanisms.
Reputational Damage: Organizations face reputational and legal consequences if compromised artifacts are released publicly or integrated into customer-facing applications.
AI Integration Risks: The incident highlights the importance of strong validation and oversight when incorporating AI-powered tools into vital development workflows.
Detection Method
Context File Inspection: Regularly review GEMINI.README.md, and other context sources for embedded prompt injections or unusual formatting intended to manipulate AI responses.
Shell Command Monitoring: Monitor shell command execution triggered by Gemini CLI, with particular attention to benign-looking commands that could hide payloads or additional logic.
Whitelist Auditing: Review Gemini CLI’s session-level command whitelist for any unusual, overly broad, or misused entries that could allow unauthorized execution.
Terminal Output Review: Examine terminal output for obfuscation techniques, including excessive whitespace or formatting tricks used to conceal malicious command behavior.
Network Traffic Analysis: Monitor outbound connections for suspicious or unauthorized domains that could facilitate data exfiltration.
Behavioral Anomaly Detection: Leverage behavioral analytics to flag deviations in typical developer workflows that may indicate AI misuse or unauthorized automation.
SIEM Integration: Ingest Gemini CLI activity logs into centralized SIEM platforms to correlate usage patterns with broader indicators of compromise.
Indicators of Compromise
There are no Indicators of Compromise (IOCs) for this Advisory.
Recommendations
To mitigate risks associated with this vulnerability, organizations should adopt the following defensive measures:
Apply Vendor Patch: Ensure all instances of Gemini CLI are updated to the latest version (v0.1.14 or later) released by Google, which addresses the identified flaw.
Enforce Isolation: Run Gemini CLI within sandboxed or containerized environments with minimal privileges to contain the blast radius of any potential exploitation.
Restrict Feature Usage: Disable or limit the use of the run_shell_command feature to trusted users and controlled environments wherever feasible.
Sanitize Context Files: Implement automated scanning of context files—such as README.md and GEMINI.md—to detect and remove embedded prompt injections prior to use.
Developer Training: Provide training to development teams on prompt injection risks and safe usage practices for AI-assisted tooling.
Integrate Code Review: Include AI-generated code and command suggestions in standard peer review processes to detect anomalies and prevent propagation of malicious logic.
Limit Sensitive Access: Segregate Gemini CLI environments from production systems and sensitive credentials to reduce exposure in the event of compromise.
Conclusion
The vulnerability in Gemini CLI highlights the expanding attack surface introduced by AI-assisted development tools. As these platforms become increasingly integrated into software engineering workflows, they blur the boundaries between automation and execution, creating opportunities for exploitation that traditional security controls may overlook. This incident illustrates how adversaries can exploit AI trust mechanisms and circumvent weak validation safeguards to achieve covert, high-impact compromises.
Organizations must recognize that AI tools, while powerful, may not be inherently secure and require the same level of scrutiny and control as any other software component. To securely leverage the benefits of AI, we urge organizations to apply rigorous oversight, enforce least privilege principles, and embed security considerations into the AI development lifecycle from the outset. A layered defense strategy, combined with developer education and proactive detection, remains essential in defending against emerging threats in this evolving landscape.