Reputational Harm - AI Security Technique
AI Security TechniqueReputational harm involves a degradation of public perception and trust in organizations. Examples of reputation-harming incidents include scandals or false impersonations.
Overview
A source-backed snapshot of this AI security technique.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0048.001
- Maturity
- demonstrated
- Priority score
- 75
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence leveldemonstrated
- Mapped defenses0 ATLAS mitigation records
- Public examples1 linked case study records
- Research risks9 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.
PoisonGPT
Researchers from Mithril Security demonstrated how to poison an open-source pre-trained large language model (LLM) to return a false fact. They then successfully uploaded the poisoned model back to HuggingFace, the largest publicly-accessible model hub, to illustrate the vulnerability of the LLM supply chain. Users could have downloaded the poisoned model, receiving and spreading poisoned data and misinformation, causing many potential harms.
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.