Record summary
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Risk profile
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"LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or granting adversary access to critical resources. For example, LLMs might prove highly effective at crafting personalized phishing emails or messages at scale that may be harder for an average user to recognize as phishing attempts (Karanjai, 2022; Hazell, 2023). In addition to being directly harmful to the targeted individual, such ‘social engineering’ attacks are often the base of larger hacking operations (Plachkinova and Maurer, 2018; Salahdine and Kaabouch, 2019)."
Suggested mitigations
Defenses that may help with related attacks.
User Training
Deepfake Detection
Source
Research source for this risk, when available.
Included resource
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
