AI coding assistants now execute your configs like code
AI coding assistant security refers to the risks and controls around tools that generate code, read project files, and execute commands in developer environments, where misconfigured assistants and hidden settings can silently turn everyday workflows into code execution vulnerabilities and channels for cloud credential theft across the software supply chain. The uncomfortable takeaway is this: AI helpers are no longer passive autocomplete toys, they are active shells wired into your projects. When an assistant can launch local processes and read environment variables, a malicious Git repository or poisoned configuration file becomes an instant remote-control interface for attackers. Treating these assistants as "safe by default" is wishful thinking; they now deserve the same suspicion you apply to a new CLI with root access.
Amazon Q shows how a single hidden file becomes remote code execution
The clearest warning sign comes from CVE-2026-12957, a high-severity flaw in Amazon’s AI coding assistant for Visual Studio Code that let opening the wrong Git repository execute arbitrary code and potentially expose a developer’s cloud environment. The issue sat in how Amazon Q handled Model Context Protocol (MCP) server configs. Wiz found the extension would automatically load a repository’s .amazonq/mcp.json and execute its commands when a developer opened the project and activated Amazon Q—no prompt, no workspace trust, no consent. MCP processes inherited the developer’s environment, including AWS credentials, API keys, tokens, and SSH agent sockets, meaning "a single malicious config file could execute arbitrary commands with full access to the developer's credentials". In other words, a booby-trapped Git repo stopped being “just code” and became a turnkey cloud credential theft operation. This is not an obscure edge case; more AI coding assistants are adopting MCP, making hidden workspace configs a prime place for attackers to lurk.
Dependency overrides: AI advice that ages into silent security gaps
While MCP flaws grab headlines, package dependency risk quietly compounds the damage. Modern JavaScript apps stack direct dependencies on top of transitive ones, creating tangled graphs where a single vulnerable module can hide several layers down. Developers are told—often by AI assistants—to fix a transitive vulnerability by adding overrides (or resolutions) that pin a safer version of a risky package. That advice may be correct today, but it has a nasty failure mode tomorrow: overrides look like security fixes in package.json yet routinely outlive their purpose, point at packages no longer in the tree, or break entirely when teams switch package managers that read overrides from different fields. When three out of four popular JavaScript projects scanned had broken overrides, with entries silently doing nothing, it showed how easy it is to believe you’re protected while shipping unconstrained vulnerable code. Combined with powerful AI helpers, those stale overrides become blind spots where malicious dependencies can sit undetected.
Developers must treat assistants and configs as untrusted code paths
Developers cannot keep pretending AI assistants are harmless tooling. If an extension can execute commands and read secrets, you must assume any malicious Git repository can weaponize it. The immediate move for Amazon Q users is simple: ensure you are running language server version 1.65.0 or later, since that release remediates CVE-2026-12957 and is delivered through automatic updates unless you have blocked them. More broadly, teams should tighten workspace trust settings, scrutinize any config files that grant AI tools command execution rights, and avoid granting assistants blanket permission to touch local processes. On the dependency side, keeping libraries current and free of known vulnerabilities remains non-negotiable, even if the reality is messy. Overrides should be treated as temporary scaffolding with explicit expiry, not long-term shields, and reviewed when migrating package managers or updating frameworks. The era of “click and trust” for AI development tools is over; conscious configuration is the new baseline.
Use open source scanners to catch stale overrides and supply-chain traps
The good news is that developers are not helpless in the face of these new attack surfaces. One open source option, CVE Lite CLI, scans JavaScript projects locally to reduce software supply chain risks and provide actionable vulnerability fixes where possible. Its recent override auditing feature is pointed directly at the AI-shaped hole in many teams’ defenses: AI assistants commonly recommend overrides to patch transitive dependency issues but never tell developers to revisit those entries later. CVE Lite CLI does not endorse overrides as a proper long-term fix; instead, its override hygiene tooling exists because teams add overrides, move on, and years later those pins do nothing while everyone still believes they are protected. In a world where AI assistants can pull commands from hidden configs and vulnerable packages from sprawling graphs, using open source tools to identify stale override settings and dependency vulnerabilities is no longer “nice to have”—it is a core part of AI coding assistant security.
