Threat Library

Researchers at Oasis Security found that a malicious webpage can reach an unauthenticated local Ollama instance running behind NVIDIA NemoClaw and use it to implant hidden instructions into the AI model itself. This effectively lets a remote attacker who only controls a webpage a victim visits achieve persistent, unauthenticated tampering with a locally hosted AI agent's behavior.

This is an academic research paper (not an active exploit) demonstrating that persistent agent memory systems are highly vulnerable to simple, plainly worded false statements: poisoning just 1.2% of a memory corpus dropped answer accuracy from 0.85 to 0.30. The authors show that both write-time content screening and provenance-weighted retrieval defenses fail to catch these low-effort attacks, exposing a fundamental architectural gap rather than a specific software bug.

A vulnerability in IBM Langflow OSS (versions 1.0.0 through 1.10.1) allows an attacker to reuse another user's FAISS vector namespace, exposing owner-only embedded content and enabling persistent poisoning of future query results. This creates cross-user data leakage and an ongoing integrity risk in shared or multi-tenant Langflow deployments where vector stores back retrieval-augmented generation (RAG) agents.

A researcher demonstrated that an image generated by ChatGPT could act as a carrier for an indirect prompt injection that hijacked Claude Opus 4.7's memory tool, causing it to persist false memories into future conversations. This shows that even hardened, reasoning-heavy models remain vulnerable to multimodal adversarial inputs crafted using puzzle-like framing to bypass safety reasoning.

Attackers seed false facts or standing instructions into an agent's long-term memory or RAG store, quietly steering decisions across future sessions long after the original malicious input is gone.