What an AI agent can do with your logged-in session
When an agent works inside your browser or desktop, it inherits everything you can do. A plain explanation of ambient authority and how to shrink it.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
When an agent works inside your browser or desktop, it inherits everything you can do. A plain explanation of ambient authority and how to shrink it.
When a vendor turns on an AI feature by default, check data flow, scope, training use, admin controls and contract terms in a week, then decide.
Tool names are unique only within one MCP server. When two servers both offer search, clients need a prefix or policy. Here is the risk and the fix.
A concrete starter set of agent tests covering scope, injection, refusals, loops, secrets and shutdown. Run each several times and record the results.
Poisoned training data changes model behavior. Practical controls for data teams: source trust, snapshots, review of changes and anomaly checks.
Fitting preprocessing on all your data inflates model scores. Why leakage matters to data governance, with the documented example and a fix.
Pressing a feedback button in Claude stores the related conversation for up to five years and may feed training. How to give feedback without oversharing.
A 2025 research paper names six agent design patterns that limit prompt injection by structure. Here is what each does and what it costs in usefulness.
Yes. Scan datasets before training, fine-tuning or evals for keys, personal data and poisoned records, because models can repeat what they were given.
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