Explore the intersection of AI and cybersecurity. Stay informed on AI-driven security trends, tools, and threats in the ever-evolving digital landscape.
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Artificial intelligence (AI) describes computer systems that perform tasks such as recognizing patterns, making predictions, understanding language, or generating content. In security reporting, the term commonly includes machine-learning models used for detection and analysis, as well as generative AI applications that produce text, code, images, or other outputs.
AI can help analyze security telemetry, prioritize vulnerabilities, and support investigations, but its outputs can be wrong or manipulated. Important attack surfaces include prompt injection that steers an application into unintended actions, sensitive data being exposed through prompts or model outputs, and excessive permissions granted to AI systems that use external tools. Models can also be degraded by poisoned training data or evaded with carefully crafted inputs. Practitioners should protect training and operational data, limit model access and tool permissions, test for adversarial behavior, and require appropriate human validation before high-impact decisions.
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As AI agents become more autonomous, strong identity, access, and auditing controls are critical to keeping them secure. The post Least privilege for AI agents: Identity, access, and tool binding appeared first on Microsoft Security Blog.
MCP tool poisoning turns trusted AI agents into a control plane for data loss. Learn how threat actors manipulate tool descriptions to trigger unauthorized actions, and how to detect, contain, and prevent it. The post Securing AI agents: When AI tools move from reading to acting appeared first on Microsoft Security Blog.
A malicious Chromium-based extension that spoofs the AI-powered answer engine Perplexity AI redirects browser search traffic using MV3 APIs and intermediary infrastructure. The post Chromium extension uses AI‑related branding to redirect browser search appeared first on Microsoft Security Blog.
What happens when threat actors target what AI remembers? Microsoft breaks down the risks and the defenses. The post Guarding AI memory appeared first on Microsoft Security Blog.
AutoJack is a novel exploit chain showing how a single malicious webpage can turn an AI browsing agent into a remote code execution vector on the host machine. By abusing trust in localhost, missing authentication, and unsafe parameter handling, attackers can trigger arbitrary process execution through AutoGen Studio’s MCP WebSocket. The research highlights a broader pattern - when agents can browse untrusted content and access local services, traditional boundaries like localhost are no longer secure. The post AutoJack: How a single page can RCE the host running your AI agent appeared first on Microsoft Security Blog.
Learn how to investigate AI activity in Microsoft 365 Copilot and Azure AI services using a structured, telemetry-driven approach. This playbook helps security teams reconstruct events, assess data exposure, and detect potential threats faster. The post Reconstructing AI activity in investigations appeared first on Microsoft Security Blog.
As threat actors operationalize AI to accelerate attacks, they are also leveraging the wider global interest around AI itself as a social engineering lure. The post AI brands as bait: How threat actors are using the AI hype in social engineering appeared first on Microsoft Security Blog.
Microsoft Threat Intelligence identified a prompt injection pathway in Claude Code GitHub Action that allowed access to workflow secrets under specific conditions. This research examines the attack chain, responsible disclosure process, Anthropic's mitigation, and guidance for securing AI-powered CI/CD workflows. The post Securing CI/CD in an agentic world: Claude Code Github action case appeared first on Microsoft Security Blog.
A surge in real-world attacks against agentic AI systems is reshaping how we think about risk. Based on 12 months of red teaming, this update introduces seven new failure modes, from supply chain compromise to goal hijacking, and the practical mitigations teams need now. The post Updating the taxonomy of failure modes in agentic AI systems: What a year of red teaming taught us appeared first on Microsoft Security Blog.
Microsoft exposes a cryptojacking campaign using SEO poisoning and ScreenConnect to target high-performance PCs, with malicious sites also surfaced through AI chatbots. The post From poisoned search results to GPU mining: A cryptojacking campaign abusing ScreenConnect and Microsoft .NET utilities appeared first on Microsoft Security Blog.
The AI systems shipping inside enterprises today are fundamentally different from the ones we were building even two years ago, because they have moved well past answering questions and into accessing your email, retrieving records from your CRM, writing and executing code, and taking actions on your behalf across dozens of connected systems. The post Introducing RAMPART and Clarity: Open source tools to bring safety into Agent development workflow appeared first on Microsoft Security Blog.
As AI agents gain autonomy, defense in depth must evolve, with application-layer design, identity, and human oversight at the center. The post Defense in depth for autonomous AI agents appeared first on Microsoft Security Blog.
Exposed UIs, weak authentication, and risky defaults could turn cloud-native AI apps on Kubernetes into potential targets by threat actors. Learn how exploitable misconfigurations lead to RCE and data leaks. The post When configuration becomes a vulnerability: Exploitable misconfigurations in AI apps appeared first on Microsoft Security Blog.
What if you could generate realistic attack telemetry on demand? Explore research methods that translate attacker behaviors (TTPs) into synthetic logs that can trigger detections at scale and without sensitive data. The post Accelerating detection engineering using AI-assisted synthetic attack logs generation appeared first on Microsoft Security Blog.