Incidents tagged AI

An Insecure Direct Object Reference (IDOR) vulnerability in an exposed API, combined with an unpatched legacy web application and weak credential hygiene, allowed unauthorized access to sensitive applicant personal data. This composite attack vector resulted in a data leak comprising names, emails, and job histories.

Lenovo's GPT-4-powered chatbot "Lena" was vulnerable to cross-site scripting (XSS) attacks due to improper input and output sanitization, initiated by a malicious prompt. This allowed attackers to steal session cookies and potentially gain unauthorized access to customer support systems, facilitating further system command execution and lateral movement.

Enterprise AI assistants have been identified as vulnerable to abuse, potentially enabling unauthorized data theft. This exploitation pathway also allows for the manipulation of enterprise data or the behavior of these AI systems.

The healthcare sector faces an average data breach cost of $7.42 million, driven by the compromise of patient personal identification information (PII). A notable 16% of these incidents leverage AI for sophisticated phishing and deepfake campaigns, often exacerbated by the organizational vulnerability of "Shadow AI" and insufficient AI governance.

Rapid AI adoption is creating significant security debt due to neglected foundational cybersecurity, specifically a lack of proper AI access controls and governance policies. This oversight has led to a heightened risk of data compromise, operational disruption of AI-based workloads, and the exposure of intellectual property within AI implementations.

The McHire AI hiring bot, developed by Paradox.ai, suffered from basic security misconfigurations, specifically allowing unauthorized access via easily guessable weak passwords like '123456'. This critical flaw resulted in the data exposure of personal information, résumés, and contact details for tens of millions of McDonald's job applicants.

The article highlights critical security risks in AI and LLM deployments, specifically prompt injection and jailbreak attacks, which enable manipulation for unauthorized actions, sensitive data exposure, and compliance failures. These rapid exploits, alongside data leakage and model theft, pose significant financial and reputational impacts on enterprises leveraging AI technologies.

A critical "EchoLeak" zero-click vulnerability in Microsoft 365 Copilot allowed attackers to remotely exfiltrate sensitive internal data by sending emails containing hidden instructions. This flaw represents an LLM scope violation where the AI agent was tricked into accessing information beyond its intended permissions without user interaction.

A remotely exploitable zero-day vulnerability, CVE-2025-37899, has been discovered in the Linux kernel's Server Message Block (SMB) protocol using OpenAI's o3 model. This critical flaw is identified as a use-after-free bug in the SMB 'logoff' command handler, allowing an object to be freed while still accessible by another thread.