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API security focuses on protecting application interfaces from unauthorized access, data exposure, abuse, and flaws in authentication or design.

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Application Programming Interfaces (APIs) are sets of rules that allow software applications to communicate and exchange data, often enabling functionality across different systems or services. APIs define how requests and responses are structured, making it possible for programs to interact without direct user involvement. In cybersecurity, APIs are commonly exposed over networks as endpoints that handle sensitive operations like data retrieval, user authentication, or transaction processing.

APIs increase the attack surface by exposing endpoints that attackers can target with unauthorized access attempts, injection attacks, or denial-of-service. Common risks include weak or missing authentication, insufficient input validation, and improper rate limiting. Effective API security requires strong authentication protocols (e.g., OAuth), strict input validation to prevent injection, rate limiting to mitigate abuse, and comprehensive logging to detect anomalies. Protecting APIs is critical to prevent data leaks, privilege escalation, and service disruption in interconnected environments.

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Office workers without AI experience warned to watch for prompt injection attacks - good luck with that Anthropic's tendency to wave off prompt-injection risks is rearing its head in the company's new Cowork productivity AI, which suffers from a Files API exfiltration attack chain first disclosed last October and acknowledged but not fixed by Anthropic.…

Cybersecurity researchers have disclosed details of a malicious Google Chrome extension that's capable of stealing API keys associated with MEXC, a centralized cryptocurrency exchange (CEX) available in over 170 countries, while masquerading as a tool to automate trading on the platform

Growing Third-Party Breach Trend Is Spreading to AI SuppliersIT organizations have built processes for reducing vendor risk, but in the AI era, that operating model is being dismantled. Modern AI environments are built on dynamic external foundational models, countless APIs, open-source components and continuous data pipelines that pose risks.