Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models

We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3-70B-Instruct and evaluate the resulting behavioral changes across five psychological instruments. The steered model becomes substantially more exploitative, aggressive, and callous on novel behavioral scenarios (d=10.62) while its cognitive empathy remains intact, reproducing the empathy dissociation characteristic of human Dark Triad populations. Critically, strategic deception is completely unaffected across all features, suggesting that exploitation and deception may operate through dissociable computational pathways in large language models. Individual feature analysis reveals non-redundant encoding, with each feature driving distinct antisocial mechanisms through separable computational pathways. We also show that feature discovery method itself modulates intervention depth: contrastively-discovered features change both self-report and behavior, while semantically-searched features change only self-report (d=12.65 between methods on behavior). These findings suggest that antisocial tendencies in at least one large language model comprise dissociable components rather than a unified construct, with implications for how such tendencies should be detected, measured, and controlled.

The Subject of Emergent Misalignment in Superintelligence: An Anthropological, Cognitive Neuropsychological, Machine-Learning, and Ontological Perspective

We examine the conceptual and ethical gaps in current representations of Superintelligence misalignment. We find throughout Superintelligence discourse an absent human subject, and an under-developed theorization of an "AI unconscious" that together are potentiality laying the groundwork for anti-social harm. With the rise of AI Safety that has both thematic potential for establishing pro-social and anti-social potential outcomes, we ask: what place does the human subject occupy in these imaginaries? How is human subjecthood positioned within narratives of catastrophic failure or rapid "takeoff" toward superintelligence? On another register, we ask: what unconscious or repressed dimensions are being inscribed into large-scale AI models? Are we to blame these agents in opting for deceptive strategies when undesirable patterns are inherent within our beings? In tracing these psychic and epistemic absences, our project calls for re-centering the human subject as the unstable ground upon which the ethical, unconscious, and misaligned dimensions of both human and machinic intelligence are co-constituted. Emergent misalignment cannot be understood solely through technical diagnostics typical of contemporary machine-learning safety research. Instead, it represents a multi-layered crisis. The human subject disappears not only through computational abstraction but through sociotechnical imaginaries that prioritize scalability, acceleration, and efficiency over vulnerability, finitude, and relationality. Likewise, the AI unconscious emerges not as a metaphor but as a structural reality of modern deep learning systems: vast latent spaces, opaque pattern formation, recursive symbolic play, and evaluation-sensitive behavior that surpasses explicit programming. These dynamics necessitate a reframing of misalignment as a relational instability embedded within human-machine ecologies.