Autonomous Cyber Weapons as AI-Enabled Digital Harms: A Platform Accountability Framework

arXiv:2610.32238v1 Announce Type: new Abstract: Governments govern AI-generated online harms from commercial platforms (EU DSA, AI Act, UK Online Safety Act), yet the most severe class remains ungoverned: autonomous cyber weapons (ACWs) deployed by nation-states against civilian infrastructure. We address this gap with three contributions: a digital-harm taxonomy (six ACW modes $\times$ five harm categories); the Meaningful Human Control (MHC) Model, a governance-compliance function with a closed-form IHL threshold and a proved weight-sensitivity bound; and a platform accountability architecture with treaty language grounded in ARSIWA state-responsibility doctrine, not Rome Statute individual responsibility. Applied to eleven documented state-attributed operations and one hypothetical defensive AI system, every documented case falls in an accountability vacuum; the threshold is target-dependent, and against wholly civilian targets no system studied clears it, including the hypothetical. Three further incidents are correctly excluded on autonomy/attribution grounds, a discriminant-validity check. The weights and threshold parameters are illustrative, not fitted.

Autonomous cyber weapons pose severe harm to civilian infrastructure, with no governance framework in place.

The research identifies that autonomous cyber weapons used by states against civilian infrastructure create a legal and accountability vacuum, signaling significant risk of harm to civilians and international security. Current governance fails to adequately address these severe AI-enabled digital harms, presenting material concerns for societal safety and rule of law.

The paper identifies autonomous cyber weapons deployed by nation-states against civilian infrastructure as a severe, ungoverned class of AI-generated harms. It proposes a framework to address the accountability vacuum, indicating that current regulations do not cover these weapons. The study finds all documented cases of state-attributed operations against civilian targets fall into an accountability vacuum.

The paper presents a framework for governing autonomous cyber weapons, which are a significant threat to civilian infrastructure. The MHC Model provides a way to ensure meaningful human control over AI systems, and the platform accountability architecture offers a path to holding states accountable for AI-enabled digital harms. The application of the framework to documented cases demonstrates its potential to fill an accountability vacuum.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   0075c033-5ea2-4bfd-9dc1-85f3c11091ed
           https://arxiv.org/abs/2610.32238
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -46  (Adverse)
   ↓
CONFIDENCE 62%