MLsec
PulseAugur coverage of MLsec — every cluster mentioning MLsec across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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ML Security Efforts Ongoing Since 2019 Amidst Emerging Threats
The article emphasizes the ongoing and significant challenge of securing machine learning (ML) systems, noting that this is not a new problem. It highlights that efforts to secure ML and engineering practices have been …
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Professor McDaniel Explains Authentic AI Security Concerns
Professor McDaniel discusses genuine AI security concerns, distinguishing them from typical vendor-focused machine learning security practices. The presentation, delivered on a rainy day, aims to provide a more authenti…
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Microsoft reportedly struggling with AI and ML security strategy
Microsoft is reportedly struggling with its AI and ML security strategies, according to reporting by Dan Goodin. The article suggests a lack of clear direction within the company regarding these critical areas.
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Frontier AI Model Dependence Poses Business Continuity Risks
Businesses that rely heavily on specific frontier AI models face significant risks to their continuity and future pricing. This dependence can lead to unforeseen limitations and increased costs as providers adjust their…
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Paper argues AI security benchmarks are not meaningful
A new paper argues that current security benchmarks for AI are not meaningful. The author suggests that these benchmarks fail to capture the real-world risks and complexities of AI systems. Instead, the paper proposes a…
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MLSec and AI Governance Discussed on Data Culture Podcast
Two users on Mastodon shared that they were guests on the Data Culture podcast. During the podcast, they discussed various aspects of Machine Learning Security (MLSec), including topics like recursive pollution and data…
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AI security risks: Recursive pollution emerges as a new threat to CISOs
A new security vulnerability known as "recursive pollution" has emerged, targeting the Chief Information Security Officer (CISO) community. This threat exploits machine learning systems, potentially impacting how securi…
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BIML identifies recursive pollution as top ML security risk
BIML identifies recursive pollution as the primary risk within machine learning security. This threat involves the potential for AI systems to become corrupted by their own outputs or by malicious data introduced during…
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New podcast series explores emerging field of ML security
The Silver Bullet podcast has launched a new series dedicated to Machine Learning Security, also known as MLsec. This series features discussions with prominent figures in the field, including Phil Venables, Giovanni Vi…
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MLSec OG Patrick McDaniel visits Berryville Machine Learning Lab
Patrick McDaniel, a prominent figure in machine learning security (MLSec), recently visited BIML. McDaniel is recognized as a foundational researcher and a significant academic leader within the MLSec field. His visit h…
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ML researchers explore recursive pollution and model collapse impacts
A discussion on Mastodon highlights the distinction between recursive pollution and model collapse in machine learning. The conversation points to a research thread exploring these concepts, suggesting significant impli…