Security & Threat Intelligence · 25.07.2026, 15:30 UTC
How GitHub’s agentic security principles make our AI agents as secure as possible
| Schweregrad | info |
|---|---|
| Kategorie | Security & Threat Intelligence |
| Quelle | GitHub Security Advisories ↗ |
| Veröffentlicht | 25.07.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
We’ve been hard at work over the past few months to build the most usable and enjoyable AI agents for developers. To strike the right balance between usability and security, we’ve put together a set of guidelines to make sure that there’s always a human-in-the-loop element to everything we design.
The more “agentic” an AI product is, the more it can actually do, enabling much richer workflows, but at the cost of a greater risk. With added functionality, there’s a greater chance and a much greater impact of the AI going off its guardrails, losing alignment, or even getting manipulated by a bad actor. Any of these could cause security incidents for our customers.
To make these agents as secure as possible, we’ve built all of our hosted agents to maximize interpretability, minimize autonomy, and reduce anomalous behavior. Let’s dive into our threat model for our hosted agentic products, specifically Copilot coding agent. We’ll also examine how we’ve built security controls to mitigate these threats, and perhaps you’ll be able to apply these principles to your own agents.
Security concerns
When developing agentic features, we are primarily concerned with three classes of risks:
Data exfiltration
When an agent has Internet access, it could leak data from the context to unintended destinations. The agent may be tricked into sending data from the current repository to an unintended website, either inadvertently or maliciously. Depending on the sensitivity of data, this could result in a severe security incident, such as if an agent leaks a write access GitHub token to a …