LLM Digest

Story

arxiv_agent_systems_research · Nov 8, 2026 · paper

Source brief

Treating the Model as an Untrusted Component: Deterministic Policy Enforcement, Human Approval and Taint Tracking Against Prompt Injection in Tool-Using Agents, with Validation on Open-Weights Models

arxiv.orgNov 8, 2026
original source linked

In brief

Tool-using LLM agents act on instructions embedded in the data they read, so prompt injection is a structural rather than a prompt-wording problem. We compare in-prompt defences (hardened prompts, spotlighting, LLM de...

Feed lens

agentevaluation

Read the original at arxiv.org →Open in live feed Read that day’s brief

Treating the Model as an Untrusted Component: Deterministic Policy Enforcement, Human Approval and Taint Tracking Against Prompt Injection in Tool-Using Agents, with Validation on Open-Weights Models

Earlier in this thread 4 items

Microsoft Moves AI Governance From Policy to Runtime Enforcement

Actions Speak Louder than Words: Measuring Cross-Lingual Policy Retention in Tool-Using Agents

SPILLWAY: Separating Target Admission from Sanitization to Defend Tool-Using Agents against Prompt Injection

How Deterministic SAST and AI Security Agents Divide the Work: Coverage, Harness Policy and Cost in Snyk VulnBench 2.0