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pro vyhledávání: '"Anderson, Hyrum"'
Autor:
Mehrotra, Anay, Zampetakis, Manolis, Kassianik, Paul, Nelson, Blaine, Anderson, Hyrum, Singer, Yaron, Karbasi, Amin
While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks. In this work, we present Tree of Attacks with Pruning (T
Externí odkaz:
http://arxiv.org/abs/2312.02119
Autor:
Carlini, Nicholas, Jagielski, Matthew, Choquette-Choo, Christopher A., Paleka, Daniel, Pearce, Will, Anderson, Hyrum, Terzis, Andreas, Thomas, Kurt, Tramèr, Florian
Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model's performance. Our attacks a
Externí odkaz:
http://arxiv.org/abs/2302.10149
Autor:
Merkhofer, Elizabeth, Chaudhari, Deepesh, Anderson, Hyrum S., Manville, Keith, Wong, Lily, Gante, João
We present the findings of the Machine Learning Model Attribution Challenge. Fine-tuned machine learning models may derive from other trained models without obvious attribution characteristics. In this challenge, participants identify the publicly-av
Externí odkaz:
http://arxiv.org/abs/2302.06716
Autor:
Apruzzese, Giovanni, Anderson, Hyrum S., Dambra, Savino, Freeman, David, Pierazzi, Fabio, Roundy, Kevin A.
Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can w
Externí odkaz:
http://arxiv.org/abs/2212.14315
Autor:
Raff, Edward, Fleshman, William, Zak, Richard, Anderson, Hyrum S., Filar, Bobby, McLean, Mark
Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may ex
Externí odkaz:
http://arxiv.org/abs/2012.09390
Child Sexual Abuse Media (CSAM) is any visual record of a sexually-explicit activity involving minors. CSAM impacts victims differently from the actual abuse because the distribution never ends, and images are permanent. Machine learning-based soluti
Externí odkaz:
http://arxiv.org/abs/2010.02387
Autor:
Raff, Edward, Zak, Richard, Munoz, Gary Lopez, Fleming, William, Anderson, Hyrum S., Filar, Bobby, Nicholas, Charles, Holt, James
Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little
Externí odkaz:
http://arxiv.org/abs/2009.03779
Autor:
Raff, Edward, Fleming, William, Zak, Richard, Anderson, Hyrum, Finlayson, Bill, Nicholas, Charles, McLean, Mark
N-grams have been a common tool for information retrieval and machine learning applications for decades. In nearly all previous works, only a few values of $n$ are tested, with $n > 6$ being exceedingly rare. Larger values of $n$ are not tested due t
Externí odkaz:
http://arxiv.org/abs/1908.00200
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A homoglyph (name spoofing) attack is a common technique used by adversaries to obfuscate file and domain names. This technique creates process or domain names that are visually similar to legitimate and recognized names. For instance, an attacker ma
Externí odkaz:
http://arxiv.org/abs/1805.09738