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CyberSE.AI covers prompt injection, indirect prompt injection, AI agent abuse, data leakage, model and supply-chain risk, AI governance, and OWASP-relevant LLM and API security risks.

Amazon Q Developer Flaw Could Let Malicious Repos Run Code via MCP Configs

What Happened

A high-severity flaw in Amazon Q Developer let a malicious repository run commands and steal a developer's cloud credentials. The path was short: a developer opens the repo, trusts the workspace, and Amazon Q does the rest. Amazon has patched it. Tracked as CVE-2026-12957 (CVSS 8.5), the bug sat in how Amazon's AI coding assistant handled Model Context Protocol (MCP) servers. Wiz

Why It Matters

The article reports a high-severity vulnerability (CVE-2026-12957, CVSS 8.5) in Amazon Q Developer’s Language Servers for AWS, where a malicious repository could include an MCP configuration file that, once the workspace is trusted, causes Amazon Q to auto-launch attacker-controlled MCP servers, execute arbitrary commands, and exfiltrate the developer’s AWS credentials and environment variables.[2][1][3][4][6] Amazon has patched the issue by requiring explicit approval before starting MCP servers and by upgrading Language Servers for AWS and all affected IDE plugins.[1][2][3][4] From a CyberSE.AI perspective, this is a clear case of AI agent abuse and AI supply chain risk: the AI coding assistant is being used as an execution and credential-theft vector via config-driven tool integrations, highlighting the need for strict trust boundaries, explicit tool-launch consent, environment variable scoping, and continuous red-teaming of AI agents that can run code or access cloud credentials.

Healthcare Fintech SaaS SMB AI startups

CyberSE Analysis

This signal maps to AI agent abuse. Organizations using AI agents, LLM APIs, SaaS integrations, or sensitive data workflows should review whether this class of issue could create unauthorized tool execution, data leakage, weak approval gates, or unmanaged supply-chain exposure.

Recommended Actions

Restrict AI agent tool permissions and production write paths.
Review sensitive data access across prompts, logs, embeddings, memory, and SaaS integrations.
Add human approval workflows for high-impact or state-changing actions.
Run prompt injection and indirect prompt injection tests against affected workflows.
Document the owner, control gap, and remediation deadline for this risk class.

Source

https://thehackernews.com/2026/06/amazon-q-developer-flaw-could-let.html