Societal Harm - AI Security Technique
AI Security TechniqueSocietal harms might generate harmful outcomes that reach either the general public or specific vulnerable groups such as the exposure of children to vulgar content.
Overview
A source-backed snapshot of this AI security technique.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0048.002
- Maturity
- realized
- Priority score
- 50
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelrealized
- Mapped defenses0 ATLAS mitigation records
- Public examples1 linked case study records
- Research risks2 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
Model Distillation Campaigns Targeting Anthropic Claude
Anthropic uncovered campaigns to extract Claude’s capabilities carried out by the three Chinese AI Labs: DeepSeek, Moonshot, and MiniMax. Collectively, these campaigns used approximately 24,000 accounts and 16 million queries. They used model distillation to train their own models on the outputs of Claude in an attempt to replicate Claude’s capabilities such as agentic reasoning, code generation, tool use, and computer use.
As outlined in Anthropic's report, model distillation was leveraged as a means for these labs to undermine Anthropic's export controls.[1] Distilled models lack the safeguards that prevent bad actors from using frontier models for malicious purposes such as the bioweapon development, disinformation, offensive cyber operations, and mass surveillance.
References
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.