arXiv subjects

Zach Miller

Publications and source records attributed to Zach Miller.

5 recordsLinked to original sources

arXiv2026

Muse Spark Safety & Preparedness Report

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, such as Muse Spark's broader content safety and behavioral profile, that are relevant to overall safety but fall outside the catastrophic risk domains governed by the Framework. Our preparedness results covering Chemical and Biological, Cybersecurity, and Loss of Control risks assess Muse Spark's deployment within Meta AI as presenting acceptable levels of residual risks under our Advanced AI Scaling Framework. We conducted a broad set of evaluations targeting dual-use and high-risk capabilities across these catastrophic risk domains. Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the "high risk" category under the Advanced AI Scaling Framework before safeguards were applied. We have implemented a multi-layered set of mitigations that address the identified risks, and Muse Spark demonstrates state-of-the-art refusal across a range of benchmarks related to hazardous workflows in chemistry and biology. We therefore release Muse Spark as the underlying model of Meta AI.

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arXiv2023

Autumn: A Scalable Read Optimized LSM-tree based Key-Value Stores with Fast Point and Range Read Speed

The Log Structured Merge Trees (LSM-tree) based key-value stores are widely used in many storage systems to support a variety of operations such as updates, point reads, and range reads. Traditionally, LSM-tree's merge policy organizes data into multiple levels of exponentially increasing capacity to support high-speed writes. However, we contend that the traditional merge policies are not optimized for reads. In this work, we present Autumn, a scalable and read optimized LSM-tree based key-value stores with minimal point and range read cost. The key idea in improving the read performance is to dynamically adjust the capacity ratio between two adjacent levels as more data are stored. As a result, smaller levels gradually increase their capacities and merge more often. In particular, the point and range read cost improves from the previous best known $O(logN)$ complexity to $O(\sqrt{logN})$ in Autumn by applying the novel Garnering merge policy. While Garnering merge policy optimizes for both point reads and range reads, it maintains high performance for updates. Moreover, to further improve the update costs, Autumn uses a small amount of bounded space of DRAM to pin/keep the first level of LSM-tree. We implemented Autumn on top of LevelDB and experimentally showcases the gain in performance for real world workloads.

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arXiv2019

SciTokens: Demonstrating Capability-Based Access to Remote Scientific Data using HTCondor

The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. SciTokens introduces a capabilities-based authorization infrastructure for distributed scientific computing, to help scientists manage their security credentials more reliably and securely. SciTokens uses IETF-standard OAuth JSON Web Tokens for capability-based secure access to remote scientific data. These access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems. In this extended abstract, we present the results over the past year of our open source implementation of the SciTokens model and its deployment in the Open Science Grid, including new OAuth support added in the HTCondor 8.8 release series.

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arXiv2018

SciTokens: Capability-Based Secure Access to Remote Scientific Data

The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems.

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arXiv2010

Flexible Session Management in a Distributed Environment

Many secure communication libraries used by distributed systems, such as SSL, TLS, and Kerberos, fail to make a clear distinction between the authentication, session, and communication layers. In this paper we introduce CEDAR, the secure communication library used by the Condor High Throughput Computing software, and present the advantages to a distributed computing system resulting from CEDAR's separation of these layers. Regardless of the authentication method used, CEDAR establishes a secure session key, which has the flexibility to be used for multiple capabilities. We demonstrate how a layered approach to security sessions can avoid round-trips and latency inherent in network authentication. The creation of a distinct session management layer allows for optimizations to improve scalability by way of delegating sessions to other components in the system. This session delegation creates a chain of trust that reduces the overhead of establishing secure connections and enables centralized enforcement of system-wide security policies. Additionally, secure channels based upon UDP datagrams are often overlooked by existing libraries; we show how CEDAR's structure accommodates this as well. As an example of the utility of this work, we show how the use of delegated security sessions and other techniques inherent in CEDAR's architecture enables US CMS to meet their scalability requirements in deploying Condor over large-scale, wide-area grid systems.

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