Multi-layer ensemble probes provide black-box adversarial robustness only if concept direction rotation patterns are model-specific not universal

Multi-layer ensemble probes improve clean-data AUROC by 29-78% over single-layer probes because deception-relevant concept directions rotate across layers rather than remaining fixed. However, this architectural improvement does not provide structural protection against adversarial attacks in the white-box case. With access to model weights and activations (the standard condition for open-weights models like Llama, Mistral, Falcon), an attacker can generalize SCAV to compute concept directions at each monitored layer and construct a single perturbation suppressing all of them simultaneously. This is a higher-dimensional optimization problem but structurally feasible by the same mechanism as single-layer SCAV. The critical unresolved question is whether black-box attacks transfer: single-layer SCAV transferred to GPT-4 because concept direction universality allowed reconstruction from different models. Multi-layer black-box SCAV requires that rotation patterns (how directions change across layers) are also universal. Beaglehole et al. found concept vectors transfer cross-language and cross-model-family, suggesting the underlying geometry may be universal enough to enable rotation pattern transfer. However, different architectures (depth, attention heads, MLP width, pre-training data) produce different residual stream dynamics, and rotation may depend on model-specific representational basis evolution. No published work tests whether multi-layer rotation patterns transfer across model families. If they do not transfer, multi-layer ensembles provide genuine black-box protection for closed-source models. If they do transfer, multi-layer ensembles merely raise attack cost without escaping the dual-use structure. This creates a deployment-context-dependent safety verdict: open-weights models remain fully vulnerable to white-box multi-layer SCAV regardless of ensemble complexity, while closed-source models may gain genuine robustness if rotation patterns are model-specific.

Extending Evidence

Source: Apollo Research publication gap analysis, April 2026

The moderating claim that multi-layer ensemble probes provide black-box robustness depends on whether rotation patterns are architecture-specific or universal. As of April 2026, no cross-model-family probe transfer testing has been published, meaning the architecture-specificity assumption remains empirically untested. The absence of this testing after 14+ months suggests either: (a) cross-family transfer is known to fail internally and not worth publishing, (b) research agendas prioritize within-family deployment robustness, or (c) the experimental setup requires infrastructure not yet built.

Extending Evidence

Source: Schnoor et al. 2025, arXiv 2509.22755

CAV-based monitoring techniques exhibit fundamental sensitivity to non-concept distribution choice (Schnoor et al., arXiv 2509.22755). The authors demonstrate that CAVs are random vectors whose distribution depends heavily on the arbitrary choice of non-concept examples used during training. They present an adversarial attack on TCAV (Testing with CAVs) that exploits this distributional dependence. This suggests cross-architecture concept direction transfer faces distributional incompatibility beyond architectural differences alone—even within a single model, CAV reliability depends on training distribution choices that would necessarily differ across model families.