CVE-2025-54795: 
JavaScript vulnerability analysis and mitigation

A critical command injection vulnerability (CVE-2025-54795) was discovered in Anthropic's Claude Code that allows attackers to bypass security restrictions and execute unauthorized commands. The vulnerability was identified by security researcher Elad Beber from Cymulate during Anthropic's Research Preview phase. The issue affects versions prior to v1.0.20 and received a CVSS score of 8.7 (High severity) (GBHackers, GitHub Advisory).

The vulnerability targets Claude Code's command execution system and stems from improper input sanitization in the echo command whitelisting mechanism. While the assistant maintains a whitelist of approved commands that execute without user confirmation, attackers can craft payloads using whitelisted commands as wrappers for arbitrary code execution. For example, attackers can terminate an echo command's string parameter and append malicious commands using the pattern: echo ""; malicious_command; echo "". Since the base command remains whitelisted, Claude Code executes the entire payload without requesting user approval (GBHackers, Cybersecurity News).

Successful exploitation of this vulnerability could allow attackers to execute arbitrary commands on the system, potentially leading to unauthorized access, data theft, or system compromise. The vulnerability is particularly concerning in enterprise environments where Claude Code might operate with elevated privileges. Attackers could potentially access sensitive configuration files and credentials or establish persistent access through backdoors (Cybersecurity News).

Anthropic has released a patch in version 1.0.20 that addresses the command injection vulnerability through improved input sanitization and granular command validation. Users on standard Claude Code auto-update received this fix automatically after release. Current users are unaffected as versions prior to 1.0.24 are deprecated and have been forced to update (GitHub Advisory).

The discovery of this vulnerability has highlighted critical challenges in securing AI-powered development tools. The recursive nature of AI-assisted vulnerability research represents a new cybersecurity paradigm where traditional security models may prove insufficient. The research underscores the need to apply rigorous security practices to AI-powered development tools while developing new approaches for AI-specific threats (Cybersecurity News).