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AI Security Technique

AI Development Workspaces - AI Security Technique

Developing and staging AI attacks often requires expensive compute resources. Adversaries may need access to one or many GPUs in order to develop an attack. They may try to anonymously use free resources such as Google Colaboratory, or cloud resources such as AWS, Azure, or Google Cloud as an efficient way to stand up temporary resources to conduct operations. Multiple workspaces may be used to avoid detection.

AI Security Techniquedemonstrated

Record summary

A quick snapshot of what this page covers.

Tactics0Attacker goals connected to this method.
Mitigations0Defenses that may help against this attack.
AI risks0Research-backed risks connected to this topic.

Attack context

How this AI attack works in practice.

ATLAS ID
AML.T0008.000
Priority score
30
Maturity: demonstrated

Mitigations

Defenses that may help against this attack.

No connected defenses. No defense is connected to this attack in the current data.

Case studies

Examples from public reports and exercises.

GPT-2 Model Replication

exercise
Date2019-08-22

OpenAI built GPT-2, a language model capable of generating high quality text samples. Over concerns that GPT-2 could be used for malicious purposes such as impersonating others, or generating misleading news articles, fake social media content, or spam, OpenAI adopted a tiered release schedule. They initially released a smaller, less powerful version of GPT-2 along with a technical description of the approach, but held back the full trained model.

Before the full model was released by OpenAI, researchers at Brown University successfully replicated the model using information released by OpenAI and open source ML artifacts. This demonstrates that a bad actor with sufficient technical skill and compute resources could have replicated GPT-2 and used it for harmful goals before the AI Security community is prepared.

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