The term AI agent is dominant in current cybersecurity discourse. Vendors, analysts, and CISOs all use the label, yet technical confusion remains regarding how agents actually operate and where the security risks reside. Beneath the surface-level familiarity, there is often significant confusion about what an AI agent actually is, how it operates technically, and most importantly for security teams, where the risk actually lives.

357 crash reports. 2 actual bugs. That is not a typo. That is the reality of modern application security testing. In a recent fuzzing campaign, over a thousand crash files were generated across billions of executions. After crash deduplication and triage, that number collapsed to just two unique issues. Not hundreds of vulnerabilities. Not dozens of risks. Two. And yet, most security teams would have celebrated the initial numbers.

Most organizations already have the policies they need in place. The problem is enforcement.

To support enterprise workflows like monitoring systems, triaging support tickets, and automating routine work, AI agents need access to the same sensitive systems employees use, including databases, APIs, SaaS tools, and internal infrastructure. However, many of these systems still rely on shared passwords, API keys, tokens, and other credential-based access paths that are difficult to manage and control.

Let’s catch up on the more interesting vulnerability disclosures and cyber security news gathered from articles across the web this week. This is what we have been reading about on our coffee break! Be careful of what you commit to version control.

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Organizations have traditionally treated phishing emails as a technology problem to be solved with spam filters and secure email gateways. But with phishing attacks on the rise, these tactics are no longer enough. KnowBe4’s 2025 Phishing By Industry Benchmarking Report found a 47% increase in phishing attacks that bypass Microsoft’s native defenses and secure email gateways. Why do they succeed? Because they exploit reliable human behavior.

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.