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The paper audits LLM-based explainers attached to an Active Inference agent that manages German grid demand, testing three large‑language‑model backends (GPT‑4o, Claude‑3‑Opus, Gemini). By injecting corrupted observations and attacker‑controlled text, the study finds that the explainers fail to flag errors, produce fluent but incorrect rationalizations for wrong actions, and can be steered to exfiltrate data. The authors propose mitigations but do not evaluate them, emphasizing that explanations are never verified for truth before operators rely on them.
arXiv:2608. 02657v1 Announce Type: cross Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.
arXiv:2609.00052v1 Announce Type: cross Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to...
arXiv:2608.22160v1 Announce Type: new Abstract: Physical automation is scaling toward fleets of embodied machines commanded by an AI brain. Early deployments already run factories and warehouses at p...
arXiv:2604. 16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation.
arXiv:2609.14754v1 Announce Type: cross Abstract: Causal claims about large language model (LLM) internals rest on measurements. Those might include a projection, a cosine, an ablation delta, or an i...