The paper On the Inseparability of Instructions and Data in Shared-Embedding Sequence Models proves prompt injection is a fundamental flaw. Instructions and data share the same embedding space in these models. You cannot rely on system prompts to secure sensitive data. Implement structural separation like data envelopes or distinct processing pipelines to protect your data vaults.
The Signal
Use structural separation to defend against injection in shared-embedding models On the Inseparability of Instructions and Data in Shared-Embedding Sequence Models. Move away from prompt-based sanitization if you handle untrusted user input. Build architectural boundaries that isolate instructions from data.
The memory-update gap causes agent instability Supersede: Diagnating and Training the Memory-Update Gap in LLM Agents. Agents often fail to integrate new information into long-term context. This failure leads to hallucinations. Implement explicit verification steps or transactional updates to keep the agent state consistent with recent interactions.
For Your Stack
Build an Agent-Native Immune System Agent-Native Immune System: Architecture, Taxonomy, and Engineering. Your authorization logic must move beyond simple API keys as you expand tool-use capabilities. Use a multi-layered defense to monitor tool-specific threats and permission escalations.
Use Self-Governing Context for long-horizon tasks Self-GC: Self-Governing Context for Long-Horizon LLM Agents. Implement a garbage collection pattern to prevent context window bloat. This pattern should proactively prune stale or irrelevant information from active memory.
Build This Week
Prototype a "Validator" pattern using Mnemosyne principles. Create a secondary, lightweight agentic loop. This loop must validate the structural integrity of the primary agent's workflows before execution against your tools.
