https://www.alephbeth.ai/tags/defense-in-depth/ Recent content in Defense-in-Depth on Aleph Beth https://www.alephbeth.ai/og-image.jpg https://www.alephbeth.ai/og-image.jpg Hugo en-US Sun, 13 Sep 2026 00:00:00 +0000 https://www.alephbeth.ai/posts/2026-09-13-hidden-weapon-in-the-page/ Sun, 13 Sep 2026 00:00:00 +0000 https://www.alephbeth.ai/posts/2026-09-13-hidden-weapon-in-the-page/ An indexed web page can carry a natural-language payload that targets, in a few lines, all four defensive layers of an AI agent: evasion, persistence, exfiltration, concealment. Worse, the outbound channel used for exfiltration is bidirectional: behind the URL, an attacker talks to the agent in real time. This article details the mechanism, shows why warning the model is a necessary but never sufficient first line, and why security comes from architecture — not from the prompt. https://www.alephbeth.ai/posts/2026-06-29-prompt-position-fine-tuning-never-validate/ Mon, 29 Jun 2026 00:00:00 +0000 https://www.alephbeth.ai/posts/2026-06-29-prompt-position-fine-tuning-never-validate/ A stubborn intuition holds that you only need to put the safety rules ‘first’ in the system prompt. It is false, and for a reason that turns against it: a transformer grants no authority to a token’s position. Fine-tuning fails exactly the same test. Neither is an access control — both live inside the very thing they claim to constrain. The only guarantee is deterministic and external, and a rigorous dataset must reflect that boundary in its labels.