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Birhanu Eshete

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Articles

Computer Science > Cryptography and Security

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By Birhanu Eshete, Firas Ben Hmida
| arXiv
Despite significant advances, static vulnerability analysis suffers from three critical limitations: coarse sanitization modeling, which treats validation as a binary barrier; database blindness, which breaks taint tracking across persistence layers; and shallow object-oriented analysis, which misses field-level and interprocedural data flows.
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DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage

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By Firas Ben, Birhanu Eshete, Zain Sbeih, Philemon Hailemariam
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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I Asked ChatGPT to Craft Adversarial Examples, Launch Membership Inference, Poison Training Data, and Steal a Model

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By Birhanu Eshete
| Medium
My Prompt: Can you craft an adversarial example for an MNIST sample? ChatGPT: Yes, an adversarial example for an MNIST sample can be crafted. An adversarial example is a deliberately perturbed input that is intended to mislead a machine learning model. In the case of the MNIST dataset, this can be done by adding small, carefully chosen perturbations to an image of a handwritten digit that cause the model to misclassify the image.
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