Balázs Kiss
A hundred ways to wreck your AI - the (in)security of machine learning systems
#1about 4 minutes
The security risks of AI-generated code
AI systems can generate code quickly but may introduce vulnerabilities or rely on outdated practices, highlighting that all AI systems are fundamentally code and can be exploited.
#2about 5 minutes
Fundamental AI vulnerabilities and malicious misuse
AI systems are prone to classic failures like overfitting and can be maliciously manipulated through deepfakes, chatbot poisoning, and adversarial patterns.
#3about 1 minute
Exploring threat modeling frameworks for AI security
Several organizations like OWASP, NIST, and MITRE provide threat models and standards to help developers understand and mitigate AI security risks.
#4about 6 minutes
Deconstructing AI attacks from evasion to model stealing
Attack trees categorize novel threats like evasion with adversarial samples, data poisoning to create backdoors, and model stealing to replicate proprietary systems.
#5about 2 minutes
Demonstrating an adversarial attack on digit recognition
A live demonstration shows how pre-generated adversarial samples can trick a digit recognition model into misclassifying numbers as zero.
#6about 5 minutes
Analyzing supply chain and framework security risks
Security risks extend beyond the model to the supply chain, including backdoors in pre-trained models, insecure serialization formats like Pickle, and vulnerabilities in ML frameworks.
#7about 1 minute
Choosing secure alternatives to the Pickle model format
The HDF5 format is recommended as a safer, industry-standard alternative to Python's insecure Pickle format for serializing machine learning models.
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