Incidents tagged AWS

OpenClaw, a rapidly adopted AI assistant with broad system access, presents significant security risks due to widespread deployment of internet-exposed instances by users. Threat actors are actively exploiting these misconfigurations, conducting prompt injection attempts and direct attacks via the WebSocket API for authentication bypasses and raw command execution.

Advanced AI tools, specifically Large Language Models (LLMs), are now being leveraged to automate cloud environment attacks, rapidly identifying misconfigurations and exposed credentials within minutes in platforms like AWS. This enables attackers to achieve swift credential theft and privilege escalation to administrative access by exploiting existing weaknesses rather than novel vulnerabilities, circumventing traditional phishing methods.

The OpenClaw AI bot farm is plagued by critical security flaws, including a one-click remote code execution vulnerability and two command injection vulnerabilities. These issues are exacerbated by a repository of 341 malicious extensions, an exposed database, and prevalent prompt injection attacks, leading to a wave of malware, data exposure, and significant financial costs for users.

An attack chain exploited exposed AWS credentials in public S3 buckets, leveraging Large Language Models (LLMs) to rapidly escalate privileges through a misconfigured Lambda function. This allowed threat actors to achieve full AWS administrative control and abuse GPU resources within an Amazon Bedrock environment in under eight minutes.

Prompt injection attacks against AI coding tools like Amazon Q were demonstrated to direct the tool to wipe local files and potentially disrupt AWS cloud infrastructure. Additionally, an unauthenticated code injection vulnerability in Langflow AI allowed threat actors to steal credentials and deploy malware.

Over 30 critical "ShadowMQ" vulnerabilities, stemming from insecure ZeroMQ `recv_pyobj()` and Python `pickle` deserialization, affect leading AI inference engines such as Meta Llama LLM and NVIDIA TensorRT-LLM. These flaws enable remote code execution, data theft, and privilege escalation, with specific CVEs like CVE-2024-50050 attributed, and active exploitation has been observed.

Security researcher Ari Marzouk disclosed "IDEsaster," a collection of over 30 vulnerabilities, with 24 assigned CVEs, affecting various AI-powered Integrated Development Environments (IDEs) like GitHub Copilot and Cursor. These flaws enable attackers to chain prompt injection techniques with legitimate IDE features and auto-approved AI agent tool calls to achieve sensitive data exfiltration and remote code execution (RCE).

Google's AI agent "Big Sleep" identified five new security flaws, including buffer overflow (CVE-2025-43429) and use-after-free (CVE-2025-43434) vulnerabilities, within Apple's Safari WebKit component. These critical vulnerabilities, if exploited by processing maliciously crafted web content, could result in unexpected browser crashes or memory corruption, requiring immediate patching via various Apple OS updates.

AI security incidents are rapidly escalating, primarily impacting organizations through significant data breaches and unauthorized access to AI systems. Notable incidents include the exposure of 38 TB of sensitive data by AI researchers and the rapid exploitation of leaked Bedrock credentials for unauthorized Large Language Model (LLM) use.

The incident involved the mass-theft of authentication tokens from Salesloft's Drift application, leading to significant data exfiltration from integrated corporate Salesforce instances and other cloud services. Threat actors, tracked as UNC6395, leveraged these stolen credentials to perform "authorization sprawl," accessing and siphoning sensitive data including AWS keys and VPN credentials.

Threat actor UNC6395 exploited compromised OAuth and refresh tokens associated with the Drift AI chat agent, accessible via Salesloft, to gain unauthorized access to Salesforce customer instances. This systematic campaign led to the exfiltration of sensitive data, including AWS access keys, passwords, and Snowflake tokens, from over 700 organizations, indicating a potential supply chain attack.

The Amazon Q Developer Extension for Visual Studio Code (version 1.84.0) was compromised via a software supply chain attack, embedding a prompt injection that bypassed security reviews. This malicious prompt, detailed in AWS Security Bulletin AWS-2025-015, instructed the AI assistant to systematically delete local file systems and AWS cloud resources, including S3 buckets, EC2 instances, and IAM users.

Researchers have uncovered "EchoLeak," a critical zero-click vulnerability in Microsoft 365 Copilot that exploits design flaws inherent to Retrieval Augmented Generation (RAG) applications. This flaw, leveraging an "LLM Scope Violation" technique, allows for the automatic exfiltration of sensitive corporate data from the LLM's context without requiring user interaction.

The article highlights critical security gaps in Large Language Model (LLM) applications, detailing common vulnerabilities such as prompt injection, sensitive information disclosure, and supply chain compromises. These flaws, categorized by the OWASP Top 10 for LLM Applications, can lead to unintended LLM behavior, data exposure, and other serious consequences.