Security

Security coverage here starts from a premise other beats elide: the AI stack is not a new attack surface so much as an old one wearing fresh abstractions. Inference servers, agent frameworks, and notebook runtimes ship with the same deserialization, SSRF, and path-traversal classes that web infrastructure spent two decades learning to harden, only now wired directly to credential stores, tool execution, and untrusted model output. The interesting question is rarely whether a given framework is exploitable; it is which inherited assumption finally broke under agentic load.

I track three structural tensions. First, the collapse of the local-host trust model as agent protocols carry developer-grade defaults into multi-tenant deployments. Second, supply-chain compromise that bypasses scanner coverage by hiding in places package auditors do not look, from model repositories to preinstall hooks to registry metadata. Third, the shrinking window between coordinated disclosure and in-the-wild exploitation, which is increasingly measured in hours and which exposes how much of the ecosystem still treats patch cadence as a quarterly concern.

The frame is comparative and skeptical rather than alarmist. Vendor lockdown modes, model-level safety training, and detector benchmarks all get evaluated against the same standard: does this address a structural property of the system, or relocate the failure mode somewhere harder to audit? Jailbreak research, disclosure-policy enforcement, and institutional credential hygiene belong on the same beat because they fail for related reasons. The work is to name those reasons in a way that still reads true after the specific advisories have rolled off the front page.