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AI API Security: Protecting Model Endpoints, Keys, Tools and Connected Services
AI applications increasingly depend on APIs for almost everything that makes them useful. The model may be accessed through an API endpoint. Retrieval may call another service. Agents may use APIs to read business data, create records, send messages or trigger workflows. Authentication may be handled by an identity provider, while billing, storage and monitoring…
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Authentication and Authorization for AI Agents: Designing Safe Permission Boundaries
AI agents are changing a familiar application-security problem into a much more complex one. Traditional software generally receives a request from a user, verifies identity, checks permissions and performs a predefined operation. An AI agent can sit between those stages. It may interpret a high-level objective, retrieve data, choose tools, make several intermediate decisions and…
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AI Vulnerability Discovery: How Security Teams Separate Real Findings From AI Noise
Artificial intelligence is making vulnerability discovery faster, but faster discovery creates a problem that security teams cannot solve by simply adding more automation. A modern AI model can review code, identify suspicious logic, connect functions across a large repository and generate technically convincing explanations of possible vulnerabilities. The result is an enormous increase in the…
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Anthropic Mythos and Project Glasswing: What Frontier AI Means for Defensive Cybersecurity
Cybersecurity has historically been constrained by one scarce resource: expert attention. Modern software contains millions of lines of code, enormous dependency trees and constantly changing infrastructure, while experienced vulnerability researchers can deeply investigate only a fraction of that attack surface. Frontier AI is beginning to change that equation. Anthropic’s Claude Mythos line represents one of…
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AI-Assisted Penetration Testing: Where Automation Helps and Where Human Researchers Matter
Penetration testing has always combined tools with human judgment. Security scanners discover exposed services, automated crawlers map applications, static analyzers inspect code and specialized utilities help researchers reproduce suspicious behavior. What is changing in 2026 is the amount of reasoning that can now be delegated to artificial intelligence. Modern AI systems can help researchers understand…
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How AI Is Changing Vulnerability Discovery and Offensive Security
Vulnerability research has traditionally been a highly manual discipline. Security researchers read source code, reverse-engineer application behavior, compare assumptions with implementation details, reproduce suspicious conditions and gradually build an understanding of how a weakness might become exploitable. Artificial intelligence is beginning to change that workflow. Modern frontier models can reason across large codebases, analyze unfamiliar…
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Adversarial Machine Learning: Evasion, Poisoning, Privacy and Misuse Attacks Explained
Machine learning systems are increasingly responsible for decisions that used to be handled entirely by deterministic software. Models classify information, detect fraud, identify malicious activity, recommend actions, generate content and increasingly interact with software through AI agents. As these systems become more important, attackers have an incentive to manipulate not only the applications surrounding the…
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How to Secure AI Tools and Function Calling Against Abuse
AI applications become significantly more powerful when a model can do more than generate text. Tool use and function calling allow an LLM or AI agent to query databases, call APIs, retrieve files, send messages, create objects and trigger application logic. In MCP, for example, tools are explicitly defined as functions that models can actively…
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MCP Security: How to Secure Model Context Protocol Servers, Tools and Authorization
MCP Security: How to Model Context Protocol has quickly become an important integration layer for AI applications and autonomous agents. Instead of building a custom connection every time an AI system needs to reach a database, development environment, business application or external service, MCP provides a standardized way for AI applications to discover data and…
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RAG Security: Protecting Retrieval-Augmented Generation Systems From Data and Prompt Attacks
Retrieval-Augmented Generation has become one of the most practical ways to connect large language models with private or frequently changing information. Instead of relying only on what a model learned during training, a RAG application searches an external knowledge source, retrieves information relevant to the current request and places that information into the model’s context…
