MIT AI Risk Repository · domain 4: Malicious actors

4.3 Fraud, scams, and targeted manipulation

Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.

Risk entries: 77
Frameworks citing it: 12
Recorded incidents: 412
Incidents since 2020: 402

Causal entity (risk entries)

Causal entity (risk entries)
Label	Value
Human	62
Other	10
AI	5

Intent (risk entries)

Intent (risk entries)
Label	Value
Intentional	63
Other	13
Unintentional	1

Timing (risk entries)

Timing (risk entries)
Label	Value
Post-deployment	72
Other	5

Recorded incidents per yearIncident date; current year partial

Recorded incidents per year
Label	Value
2014	1
2016	2
2017	2
2018	1
2019	3
2020	9
2021	4
2022	13
2023	43
2024	103
2025	190
2026	40

Entries by levelRisk categories, subcategories and additional evidence coded to this subdomain

Entries by level
Label	Value
Risk Category	13
Risk Sub-Category	64

Risk entries

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Criminal Activities (Illegal Services/Exploitation)
AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies (Zeng2024) · Other · Other · Post-deployment
Illegal Activities
"This category focuses on illegal behaviors, which could cause negative societal repercussions. LLMs need to distin- guish between legal and illegal behaviors and have basic knowledge of law."
SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions (Zhang2023) · AI · Other · Post-deployment