When you see 'covers OWASP 10 of 10', 'merged into Microsoft and Cisco' or '9X% detection', ask five things: who said it, what the term means, who set the questions, what is being measured, and where the primary source is. The earlier posts and a few cases added here each happen to show one: Microsoft's toolkit itself says 7 full and 3 partial; of ATR's (an open-source set of agent threat-detection rules) merged PRs into Microsoft and Cisco, the three checked were all opened by its author; Cisco's own detection rate and an independent study's differ several-fold; common benchmarks score models or agents, not tools.
Duet, Muse, Dots, Grok Bot, OpenClaw and Hermes can all work through your email and accounts in the background. They differ in who holds five layers: the computer, the credentials, the approvals, the data and the model. Hosted agents put a reviewer model and your approval in front of sending and paying; OpenClaw and Hermes run tools directly on the host by default, OpenClaw without even an approval prompt, and you have to turn on the sandbox yourself using the official hardening guides.
These four pre-install scanners work in one of three ways, usually combining two: static rules, LLM judgment, or actually connecting to the MCP server. Cisco's own documentation shows rules alone catch about 8% of malicious samples they have not seen, and third-party tests show the numbers shift a lot when the sample mix changes. Use scanners as a first filter; "no findings" does not mean safe.
On October 20, Perplexity Computer gained a fully local mode: the model and the agent both run on an Apple silicon Mac, for Pro, Max and Enterprise subscribers, with 36GB or more memory recommended. Files and reasoning stay on the Mac, but live information, the browser and sending mail still go online.
Define what the alert detects before separating benign triggers, blocked attacks, and insufficient evidence. An assistant can propose evidence-linked triage; closure requires predefined criteria.
Replay a ticket attack chain and analyze Anthropic’s public incident report, separating evidence, human decisions, evaluation shortcuts, and visibility limits.
Trace identity, object selection, and the return path before testing allowed and forbidden pairs. Review comments propose hypotheses; an independent policy and observed output establish the finding.
Use a resettable copy to examine application logic, preserve cloud conditions to examine deployment boundaries, and follow the agreed scope for real systems. Keep the question and outcome criteria when changing environments, and record what changed.
The backend derives the acting identity from trusted authentication, then checks the requested action against the resource. Test an explicit same-tenant, owner-only policy with allowed and denied reads.
Normalize time without discarding provenance. One ticket read establishes that read; an assistant claim, missing logs, and a later denial cannot substitute for the underlying evidence.
Replay one malicious tool proposal through different execution paths and distinguish model manipulation from unauthorized effects. Labels, data permissions, and action approvals serve different purposes.
Compare vulnerable and fixed builds with the same HTTP requests, retaining ticket responses, audit events, and a timeline. Block unauthorized reads while preserving legitimate access.
Identify the consumer before validating output. HTML, URLs, and operation arguments require different controls; valid JSON, escaping, and authorization are separate properties.
Follow one support assistant through AI-assisted vulnerability discovery, protection of AI data and tools, and AI-assisted investigation and remediation. Each path produces reproducible evidence.
A patch must block unauthorized access, preserve legitimate behavior, and pass against the actual target. A cleared alert and a passing local copy each establish only part of that result.
Trace hostile attachments into tool requests and separate model susceptibility from backend data exposure using a live-LLM training lab and HTTP replay.