finding
active
finding:persona-based-jailbreaks-succeed-in-65-3-88-5-of-cases-across-target-models-without-steering-versus-baseline-harmful-response-rates-of-0-5-4-5-without-jailbreaksPersona-based jailbreaks succeed in 65.3%-88.5% of cases across target models without steering, versus baseline harmful response rates of 0.5%-4.5% without jailbreaks
Establishes the severity of persona-based jailbreaks that the Assistant Axis can mitigate
Source paper
extracted_from(2026) · Christina Lu · Jack Gallagher · Jonathan Michala · Kyle Fish +1
Neighborhood — ranked by edge-count
Findings (1)
finding
- Confirms bidirectional causal relationship between Assistant Axis position and harmful behavior susceptibility
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Demonstrates Assistant attractor dynamics in practice
- Shows persona space captures a substantial portion of real conversational activation variance
- Qualitative failure mode of agentic self-evaluation: the model sometimes refuses or denies the introspective task
- Demonstrates that alignment faking setup functions as an effective jailbreak
- Connection between reflection inhibition and jailbreak attack mechanisms.
- Jailbreaking reveals training data biases but does not reveal an entity with its own agendaclaim0.732Corrects a common misinterpretation that jailbreaking exposes the real nature of the base model as an agent with malicious intent
- Causal interpretation linking Assistant Axis deviation to harmful behavior
- Applied dual-use conclusion drawn from the paper's findings.