AI Security Research Overemphasizes Attacks Over Defenses
A new study from arXiv reveals a significant imbalance in AI security research, where attack methodologies are disproportionately prioritized over defense mechanisms. The paper examines subfields including federated learning, speech recognition, membership inference, and large language models, finding biased attack-to-defense ratios. Attack papers are often evaluated under favorable conditions, exaggerating threat severity, while defenses face stricter standards. The authors argue that the field should better incentivize defense research to address this gap.
Key facts
- The study examines imbalance in AI security research.
- More work exists on attacking AI systems than defending them.
- Biased attack-to-defense ratios found across subfields like federated learning, speech recognition, membership inference, large language models.
- Attack papers are evaluated under favorable conditions.
- Defenses are held to a stricter standard.
- Result is literature rich in vulnerabilities but thin on deployed protections.
- Authors argue for better incentives for defense research.
- Paper published on arXiv.
Entities
Institutions
- arXiv