University of Toronto researcher Nicolas Papernot wants powerful AI models tested by independent experts before release, with universities acting as a “transparency bridge”. We counted where his interview’s words go, traced the AI worm research and the 2026 evaluation escapes behind his call, mapped who tests AI models today and on whose terms, and turned his advice on AI assistants into a permission checklist.
Instinct email addresses went live on 8 September 2026, and the pitch was simple enough to fit in a single post: your assistant gets its own mailbox, so it can open accounts, chase businesses and handle the admin that arrives as mail. Founder Noah Shinn announced it on X in 141 words. Seven of those […]
A VentureBeat guest essay argues that AI agents which pass authentication can still drift from their objective, expose data and have their memory poisoned, because authentication verifies who an agent is while runtime trust verifies what it is doing. This piece explains the five runtime threats, the five-pillar runtime trust model (intent validation, behavioural monitoring, policy enforcement, least-privilege execution, human oversight), how it maps onto the six deployment gates in the companion essay, the 2026 research measuring memory-poisoning success rates, the OWASP and MITRE ATLAS entries that name each threat, the survey figures on the post-authentication control gap, what Microsoft, AWS and Cloudflare have shipped, and a roadmap a UK business can run with the tools it already owns.
Copilot agents are the point at which Microsoft’s AI stops being an assistant that answers questions and becomes software that acts on your behalf. That shift is small in the interface and enormous everywhere else. An assistant that drafts an email is a productivity feature. An agent that reads a mailbox, decides something and sends […]
Prompt injection is the one attack class against business AI that has no structural fix, because instructions and data reach a language model through the same channel. This guide turns that into something you can manage: where injected instructions actually enter a business system, how to scope an assessment so it finishes in days, a ten-question likelihood and impact matrix anchored to observable facts, a control map showing which measures leave a low residual and which depend on the model behaving, how to build an injection corpus and test the boundary rather than the model, how to record findings in a register an auditor can follow, who owns the risk and what UK and EU regulators expect, realistic costs, and a 90-day plan.
The moment you connect a language model to a ticketing API, a finance system, a mailbox or a shell, you stop shipping a chat feature and start shipping a new class of privileged user. This guide covers the engineering work that keeps that user contained: how to classify and scope tools into risk tiers, why an agent needs its own identity and short-lived credentials rather than a shared service account, how to contain the blast radius of a single compromised run with sandboxing and default-deny egress, where to place human approval gates so they are decisive rather than theatre, what actually works against indirect prompt injection arriving through retrieved content, what to log so an incident is investigable, how to test the controls before launch, and a 90-day rollout plan with realistic costs and named owners.
Most AI systems reach launch having been tested only by people trying to make them work. Adversarial testing asks the other question: what happens when someone actively tries to make the system misbehave. This guide sets out what to test before go-live — how to scope the exercise and define harm in your own domain, the five attack classes that matter for business deployments, whether to run it internally, buy it in or automate it, how to score findings so severity cannot be renegotiated after the fact, which fixes actually hold, what the work costs in person-days and pounds, and the evidence pack that answers an enterprise security questionnaire and maps onto the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.
A practical guide to monitoring AI agents in production: the four signal groups that matter, run tracing, quality drift detection, prompt-injection and tool-abuse signals, cost per successful outcome, and a staged 90-day rollout.
A practical playbook for testing AI agents before launch: build a real evaluation set, red-team prompt injection and tool abuse, prove reliability under load, and gate every release on objective checks.
AI Agent Memory Poisoning is emerging as one of the most significant security threats facing autonomous artificial intelligence systems with persistent memory capabilities. Artificial intelligence agents are becoming increasingly autonomous. Learn more about secure enterprise AI through Microsoft AI. Modern AI systems can browse the web, retrieve documents, maintain long-term memory, interact with software applications, […]
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