Side-Channel Attack Risks in Shared Sandbox Infrastructure
Shared hardware lets attackers steal secrets through timing and cache without breaking encryption.
Section
16 stories in Sandbox Isolation Primitives.
Shared hardware lets attackers steal secrets through timing and cache without breaking encryption.
AI agents burst memory unpredictably, breaking cgroup v2's assumptions built for stable workloads.
Reducing unnecessary Linux capabilities closes the attack surface before an exploit lands.
Hardware isolation lets untrusted AI-generated code run without risking the entire host system.
Rootless containers eliminate privilege escalation paths AI code execution inherently risks.
Layered filesystem controls matter more than any single mechanism when sandboxing AI-generated code.
AI pipeline images need multi-layered scanning beyond standard container tools.
AI agents generated at runtime demand isolation layers beyond standard Docker containers.
How to build seccomp profiles that actually work for AI agents running code.
Different scanners catch different vulnerabilities; here's how to pick the right one.
AI agents write code at runtime, breaking Docker's security model.
Catch vulnerable Docker images before they reach production with CI pipeline scanning.
Trivy, Grype, and Snyk handle AI's rapid image churn differently than traditional pipelines require.
PIE closes the biggest gap in address randomization that attackers have exploited for years.
Execution-layer MCP servers need isolation infrastructure, not just hardening practices.
Namespaces control visibility, not security, and confusing the two enables container escapes.