Dmitrijs Trizna
@ditriznaIndependent Cyber-Security and AI Researcher | ex-Microsoft. I have two Master's Degrees, almost Ph.D., OSCP, GREM, GDAT, etc.
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@MSFTBlueHat organized awesome event for the first time coming in India — was happy to be part of it! Recording of the talk is coming ✌️
Dmitrijs Trizna (@ditrizna), Senior Security Researcher at Microsoft, is presenting his #BlueHatIndia talk: “The Impact of Backdoor Poisoning Vulnerabilities on AI-Based Threat Detectors.” In his talk, Dmitrijs discussed AI-based defenses: threat model: living-off-the-land, data…
This Notebook should be of interest to any AI Red Teamer, since has all the details about SOTA model that is evaded only with “human interpretable” adversarial perturbations!
A stand-alone replication Colab for **Ensemble Everything Everywhere** is out! 🎉 ⚔️You train & attack a SOTA-like multi-resolution self-ensemble from our paper /Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness/ 🔗 Links in the first reply!
This is awesome role and team! If you are in US, consider this position working with @Cyb3rWard0g!
📣 As part of the Autonomous Defense & Protection Team (ADAPT), my team is growing and taking shape with recent hires, and I’m excited to announce a new opportunity to join us! 🔥Apply here ➡️ jobs.careers.microsoft.com/global/en/job/… 🚀 I’m hiring a Principal Applied Scientist to support our…
Hah, this is gold!
That’s why I say to people: “LLM’s will not find you 0-days.” LLMs indeed can be helpful to identify known weaknesses present in the training dataset, or as N-shot examples in RAG, but as @niklas2484 et al. show, LLMs will fail to generalize even on those examples.
LLMs recently achieved impressive results on vulnerability detection datasets -- but how good are they really? Paper: arxiv.org/abs/2306.17193 Code: github.com/niklasrisse/Li… w/ @mboehme_ (1/13)
If you're reading this tweet while in @BlackHatEvents, join me and @ditrizna in our journey through ML malware detection and adversarial pentesting! 😈 August 3/4 and 5/6! See you there 😎 blackhat.com/us-24/training… #blackhat #malware #detection #ai #machinelearning
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