Abstract Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and healthcare that operate under strict policies. To address this limitation, we launched Automated Reasoning checks (ARc) : a public service that (1) uses LLMs with optional human guidance to formalize natural language policies, allowing fine-grained control of the formalization process, and (2) uses inference-time autoformalization to validate logical correctness of natural language statements against those policies. ARc performs multiple redundant formalization steps at inference time, checking the formalizations for semantic equivalence. Our benchmarks show that ARc exceeds 99% soundness and achieves a near-zero false positive rate in identifying logical validity. Our approach produces auditable artifacts that substantiate the verification outcomes and can be used to improve the original text. ARc is the first commercial offering from a major cloud provider to integrate automated reasoning into a generative AI guardrail.

Batfish is a tool to analyze network configurations and forwarding. It has evolved from a research prototype to an industrial-strength product, guided by scalability, fidelity, and usability challenges encountered when analyzing complex, real-world networks. We share key lessons from this evolution, including how Datalog had significant limitations when generating and analyzing forwarding state and how binary decision diagrams (BDDs) proved highly versatile. We also describe our new techniques for addressing real-world challenges, which increase Batfish performance by three orders of magnitude and enable high-fidelity analysis of networks with thousands of nodes within minutes.

As software-defined networking deployments mature, operators need to manage and compose multiple resource-management applications, such as traffic engineering and service chaining. Today such applications' resource management algorithms run separately and composition approaches are output-driven, e.g., running each application on a statically provisioned slice of the network and then combining the flow rules output for each slice. Such approaches result in inefficient resource utilization and unfairness. Instead, we argue for intent-driven composition, where a unified resource optimization formulation is composed from applications' high-level intents and the solution to this problem formulation is realized in the network. We design Chopin1, an intent-driven framework for composing SDN resource-management applications. Chopin's design addresses key robustness challenges with regard to efficiency and fairness that arise in realizing such an intent-driven approach. We have integrated Chopin with the ONOS controller and show that it substantially improves efficiency and fairness over existing composition approaches.

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices. Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.

Vuze is a popular file‐sharing client. When looking for content, Vuze selects from its list of neighbours, a set of 20 nodes to be contacted; the selection is performed such that the neighbours closest to the content in terms of Vuze ID are contacted first. To improve efficiency of its searches, Vuze implements a network coordinate system: from the set of 20 to‐be‐contacted nodes, queries are sent to the closest nodes in terms of network distance, which is calculated by the difference in network coordinates. However, network coordinate systems are inherently insecure and a malicious peer can lie about its coordinate to appear closest to every peer in the network. This allows the malicious peer to bias next‐hop choices for victim peers such that queries will be sent to the attacker, thus hijacking every search query. In our experiments, almost 20% of the search queries are hijacked; the cost of performing this attack is minimal – less than $112/month.

We present an epidemiological study of malware encounters in a large, multi-national enterprise. Our data sets allow us to observe or infer not only malware presence on enterprise computers, but also malware entry points, network locations of the computers (i.e., inside the enterprise network or outside) when the malware were encountered, and for some web-based malware encounters, web activities that gave rise to them. By coupling this data with demographic information for each host's primary user, such as his or her job title and level in the management hierarchy, we are able to paint a reasonably comprehensive picture of malware encounters for this enterprise. We use this analysis to build a logistic regression model for inferring the risk of hosts encountering malware; those ranked highly by our model have a >3x higher rate of encountering malware than the base rate. We also discuss where our study confirms or refutes other studies and guidance that our results suggest.

As traffic volumes and the types of analysis grow, network intrusion detection systems (NIDS) face a continuous scaling challenge. Management realities, however, limit NIDS hardware upgrades to occur typically once every 3-5 years. Given that traffic patterns can change dramatically, this leaves a significant scaling challenge in the interim. This motivates the need for practical solutions that can help administrators better utilize and augment their existing NIDS infrastructure. To this end, we design a general architecture for network-wide NIDS deployment that leverages three scaling opportunities: on-path distribution to split responsibilities, replicating traffic to NIDS clusters, and aggregating intermediate results to split expensive NIDS processing. The challenge here is to balance both the compute load across the network and the total communication cost incurred via replication and aggregation. We implement a backwards-compatible mechanism to enable existing NIDS infrastructure to leverage these benefits. Using emulated and trace-driven evaluations on several real-world network topologies, we show that our proposal can substantially reduce the maximum computation load, provide better resilience under traffic variability, and offer improved detection coverage.

A network coordinate system assigns Euclidean “virtual” coordinates to every node in a network to allow easy estimation of network latency between pairs of nodes that have never contacted each other. These systems have been implemented in a variety of applications, most notably the popular Vuze BitTorrent client. Zage and Nita-Rotaru (at CCS 2007) and independently, Kaafar et al. (at SIGCOMM 2007), demonstrated that several widely-cited network coordinate systems are prone to simple attacks, and proposed mechanisms to defeat these attacks using outlier detection to filter out adversarial inputs. Kaafar et al. goes a step further and requires that a fraction of the network is trusted. More recently, Sherr et al. (at USENIX ATC 2009) proposed Veracity, a distributed reputation system to secure network coordinate systems. We describe a new attack on network coordinate systems, Frog-Boiling, that defeats all of these defenses. Thus, even a system with trusted entities is still vulnerable to attacks. Moreover, having witnesses vouch for your coordinates as in Veracity does not prevent our attack. Finally, we demonstrate empirically that the Frog-Boiling attack is more disruptive than the previously known attacks: systems that attempt to reject “bad” inputs by statistical means or reputation cannot be used to secure a network coordinate system.