Untrusted Content Masking for Web Agents with Security Guarantees
Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data. In text-based environments such as tool-use APIs, this separation arises naturally: agents can reason from interface definitions without ever processing untrusted content. Extending these guarantees to web agents faces a fundamental challenge: to perceive and interact with their environment, web agents must first observe the rendered page, which intermingles trusted content with untrusted content. This structural entanglement removes the trust bou
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 53%Hackers Abuse SEO Poisoning and Hidden HTML to Trick AI Agents Into Following Malicious Instructions - CyberSecurityNews →
- PossiblePossibly related (embedding) · 53%How to Secure AI Agents With Container Sandboxing - HackerNoon →
- PossiblePossibly related (embedding) · 48%What Platform Security Taught Me About Trusting LLM Agents - Communications of the ACM →
- PossiblePossibly related (embedding) · 47%Omnigent: Open-source AI agent framework and meta-harness - Help Net Security →
- PossiblePossibly related (embedding) · 47%joshuaswarren/remnic →
- PossiblePossibly related (embedding) · 47%SpyGlace Attacks Abuse Trusted Developer Services to Evade Network Detection - CyberSecurityNews →
- LinkedLinked via arxiv author · 85%Kristina Nikolić →
“Untrusted Content Masking for Web Agents with Security Guarantees”
- LinkedLinked via arxiv author · 85%Egor Zverev →
“Untrusted Content Masking for Web Agents with Security Guarantees”