The widespread adoption of Large Language Models (LLMs) is driving the rapidly growing demand for large-scale computations like training and fine-tuning models. In many areas, the confidentiality of the underlying data is of critical importance to their corporate or government owners. However, securing data in large-scale computations is challenging. First, its demand for enormous hardware resources typically requires outsourcing (e.g., to the public cloud). Second, the large and rapidly evolving software stack used in LLM training in conjunction with a growing incidence of supply chain attacks and software vulnerabilities makes it all but impossible for data owners to establish trust in the code that processes their highly sensitive data. Confidential computing and sandboxing are promising techniques for solving these problems. However, existing sandboxes do not address covert channels which limits their ability to protect confidential data.

Kernel debloating is a practical mechanism to mitigate the security problems of the operating system kernel by reducing its attack surface. Existing kernel debloating mechanisms focus on specializing a kernel to run a target application based on its dynamic traces collected in the past - they remove functions from the kernel which are not used by the application according to the traces. However, since the dynamic traces do not ensure full coverage, false removals of required functions are unavoidable. This paper proposes Hacksaw, a novel mechanism to debloat a kernel for a target machine based on its hardware device inventory. Hacksaw accurately debloats a kernel without false removals because figuring out which hardware components are attached to the machine as well as which device drivers manage them is comprehensive and deterministic. Hacksaw removes not only inoperative device drivers that do not control any attached hardware components but also other kernel modules and functions which are associated with the inoperative drivers according to three dependency analysis approaches: call-graph, driver-model, and compilation-unit analyses. Our evaluation shows that Hacksaw effectively removes inoperative kernel modules and functions (i.e., their respective reduction ratios are 45% and 30% on average) while ensuring validity and compatibility.

Space sharing services like vacation rentals are being equipped with smart devices. However, sharing of such devices has privacy and security problems due to no or unclear control transfer between owners and users. In this paper, we propose Spacelord, a system to time-share smart devices contained in a shared space privately and securely while allowing users to configure them. When a user stays at a space, Spacelord ensures that the smart devices contained in it run code and configurations the user trusts while removing pre-installed code and configurations. When the user leaves the space, Spacelord reverts any changes the user has introduced to the smart devices to delete remaining private data and let the owner take back control over the devices. We evaluate Spacelord for two realistic space-sharing cases—smart home and coworking meeting room—and observe reasonable provisioning delay and runtime overhead.

Audit systems maintain detailed logs of security-related events on enterprise machines to forensically analyze potential incidents. In principle, these logs should be safely stored in a secure location (e.g., network storage) as soon as they are produced, but this incurs prohibitive slowdown to a monitored machine. Hence, existing audit systems protect batched logs asynchronously (e.g., after tens of seconds), but this allows attackers to tamper with unprotected logs.This paper presents HARDLOG, a practical and effective system that employs a novel audit device to provide fine-grained log protection with minimal performance slowdown. HARDLOG implements criticality-aware log protection: it ensures that logs are synchronously protected in the audit device before an infrequent security-critical event is allowed to execute, but logs are asynchronously protected on frequent non-critical events to minimize performance overhead. Importantly, even on non-critical events, HARDLOG ensures bounded-asynchronous protection: it sends log entries to the audit device within a tiny, bounded delay from their creation using well-known real-time techniques. To demonstrate HARDLOG’S effectiveness, we prototyped an audit device using commodity components and implemented a reference audit system for Linux. Our prototype achieves a bounded protection delay of 15 milliseconds at non-critical events alongside undelayed protection at critical events. We also show that, for diverse real-world programs, HARDLOG incurs a geometric mean performance slowdown of only 6.3%, hence it is suitable for many real-world deployment scenarios.

We live in a time when billions of IoT devices are being deployed and increasingly relied upon. This makes ensuring their availability and recoverability in case of a compromise a paramount goal. The large and rapidly growing number of deployed IoT devices make manual recovery impractical, especially if the devices are dispersed over a large area. Thus, there is a need for a reliable and scalable remote recovery mechanism that works even after attackers have taken full control over devices, possibly misusing them or trying to render them useless.

Knowledge work increasingly spans multiple computing surfaces. Yet in status quo user experiences, content as well as tools, behaviors, and workflows are largely bound to the current device-running the current application, for the current user, and at the current moment in time. SurfaceFleet is a system and toolkit that uses resilient distributed programming techniques to explore cross-device interactions that are unbounded in these four dimensions of device, application, user, and time. As a reference implementation, we describe an interface built using SurfaceFleet that employs lightweight, semi-transparent UI elements known as Applets. Applets appear always-on-top of the operating system, application windows, and (conceptually) above the device itself. But all connections and synchronized data are virtualized and made resilient through the cloud. For example, a sharing Applet known as a Portfolio allows a user to drag and drop unbound Interaction Promises into a document. Such promises can then be fulfilled with content asynchronously, at a later time (or multiple times), from another device, and by the same or a different user.

The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade. Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic. While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities. This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction. Like traditional computers, IoT devices are subject to attack and compromise. Large IoT deployments consisting of many nearly identical devices are especially attractive targets. At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area. In the worst case, technicians have to travel to all devices and manually recover them. Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices. This paper presents Cider, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment. The recovery requires minimal manual intervention. After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct Cider to force the devices to reset and to install the patched firmware on the devices. We demonstrate the universality and practicality of Cider by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end. Our evaluation shows that the performance overhead of Cider is generally negligible.

Many software providers operate crash reporting services to automatically collect crashes from millions of customers and file bug reports. Precisely triaging crashes is necessary and important for software providers because the millions of crashes that may be reported every day are critical in identifying high impact bugs. However, the triaging accuracy of existing systems is limited, as they rely only on the syntactic information of the stack trace at the moment of a crash without analyzing program semantics.

Traffic exchange services enable members to bring traffic to their websites from a diverse pool of IP addresses, in return for visiting sites of other members. We examine the world of traffic exchanges to characterize their makeup, usage, and monetization. We find that the ecosystem includes a range of services, from manual exchanges where participants must solve CAPTCHAs between successive page views, to exchanges that provide tools that automatically surf without requiring any user action. By "milking" a sample of these exchanges, we analyze month-long datasets to examine the nature of URLs that members submit to them. We find a wide prevalence of URLs for services that pay users in return for views to their content, and at least 30% of the requested impressions are for pages that clearly participate in a class of impression fraud called referrer spoofing. We also analyze the size and composition of a sample of these exchange networks by making purchases, finding that the exchanges delivered visits from roughly 200K unique IP~addresses, and that in some exchange networks, the majority of visits came from cloud hosting services.

We present VC3, the first system that allows users to run distributed MapReduce computations in the cloud while keeping their code and data secret, and ensuring the correctness and completeness of their results. VC3 runs on unmodified Hadoop, but crucially keeps Hadoop, the operating system and the hyper visor out of the TCB, thus, confidentiality and integrity are preserved even if these large components are compromised. VC3 relies on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running within isolated regions, to prevent attacks due to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and 8% with read/write integrity.

The presence of large numbers of security vulnerabilities in popular feature-rich commodity operating systems has inspired a long line of work on excluding these operating systems from the trusted computing base of applications, while retaining many of their benefits. Legacy applications continue to run on the untrusted operating system, while a small hyper visor or trusted hardware prevents the operating system from accessing the applications' memory. In this paper, we introduce controlled-channel attacks, a new type of side-channel attack that allows an untrusted operating system to extract large amounts of sensitive information from protected applications on systems like Overshadow, Ink Tag or Haven. We implement the attacks on Haven and Ink Tag and demonstrate their power by extracting complete text documents and outlines of JPEG images from widely deployed application libraries. Given these attacks, it is unclear if Over shadow's vision of protecting unmodified legacy applications from legacy operating systems running on off-the-shelf hardware is still tenable.

Today’s cloud computing infrastructure requires substantial trust. Cloud users rely on both the provider’s staff and its globally distributed software/hardware platform not to expose any of their private data. We introduce the notion of shielded execution, which protects the confidentiality and integrity of a program and its data from the platform on which it runs (i.e., the cloud operator’s OS, VM, and firmware). Our prototype, Haven, is the first system to achieve shielded execution of unmodified legacy applications, including SQL Server and Apache, on a commodity OS (Windows) and commodity hardware. Haven leverages the hardware protection of Intel SGX to defend against privileged code and physical attacks such as memory probes, and also addresses the dual challenges of executing unmodified legacy binaries and protecting them from a malicious host. This work motivated recent changes in the SGX specification.

Web applications are constantly under attack. They are popular, typically accessible from anywhere on the Internet, and they can be abused as malware delivery systems.

Fay is a flexible platform for the efficient collection, processing, and analysis of software execution traces. Fay provides dynamic tracing through use of runtime instrumentation and distributed aggregation within machines and across clusters. At the lowest level, Fay can be safely extended with new tracing primitives, including even untrusted, fully optimized machine code, and Fay can be applied to running user-mode or kernel-mode software without compromising system stability. At the highest level, Fay provides a unified, declarative means of specifying what events to trace, as well as the aggregation, processing, and analysis of those events. We have implemented the Fay tracing platform for Windows and integrated it with two powerful, expressive systems for distributed programming. Our implementation is easy to use, can be applied to unmodified production systems, and provides primitives that allow the overhead of tracing to be greatly reduced, compared to previous dynamic tracing platforms. To show the generality of Fay tracing, we reimplement, in experiments, a range of tracing strategies and several custom mechanisms from existing tracing frameworks. Fay shows that modern techniques for high-level querying and data-parallel processing of disagreggated data streams are well suited to comprehensive monitoring of software execution in distributed systems. Revisiting a lesson from the late 1960s [Deutsch and Grant 1971], Fay also demonstrates the efficiency and extensibility benefits of using safe, statically verified machine code as the basis for low-level execution tracing. Finally, Fay establishes that, by automatically deriving optimized query plans and code for safe extensions, the expressiveness and performance of high-level tracing queries can equal or even surpass that of specialized monitoring tools.

Fay is a flexible platform for the efficient collection, processing, and analysis of software execution traces. Fay provides dynamic tracing through use of runtime instrumentation and distributed aggregation within machines and across clusters. At the lowest level, Fay can be safely extended with new tracing primitives, including even untrusted, fully-optimized machine code, and Fay can be applied to running user-mode or kernel-mode software without compromising system stability. At the highest level, Fay provides a unified, declarative means of specifying what events to trace, as well as the aggregation, processing, and analysis of those events.

Dynamic kernel data have become an attractive target for kernel-mode malware. However, previous solutions for checking kernel integrity either limit themselves to code and static data or can only inspect a fraction of dynamic data, resulting in limited protection. Our study shows that previous solutions may reach only 28% of the dynamic kernel data and thus may fail to identify function pointers manipulated by many kernel-mode malware.

Bugs in kernel extensions remain one of the main causes of poor operating system reliability despite proposed techniques that isolate extensions in separate protection domains to contain faults. We believe that previous fault isolation techniques are not widely used because they cannot isolate existing kernel extensions with low overhead on standard hardware. This is a hard problem because these extensions communicate with the kernel using a complex interface and they communicate frequently. We present BGI (Byte-Granularity Isolation), a new software fault isolation technique that addresses this problem. BGI uses efficient byte-granularity memory protection to isolate kernel extensions in separate protection domains that share the same address space. BGI ensures type safety for kernel objects and it can detect common types of errors inside domains. Our results show that BGI is practical: it can isolate Windows drivers without requiring changes to the source code and it introduces a CPU overhead between 0 and 16%. BGI can also find bugs during driver testing. We found 28 new bugs in widely used Windows drivers.

Recent work has established the importance of automatic reverse engineering of protocol or file format specifications. However, the formats reverse engineered by previous tools have missed important information that is critical for security applications. In this paper, we present Tupni, a tool that can reverse engineer an input format with a rich set of information, including record sequences, record types, and input constraints. Tupni can generalize the format specification over multiple inputs. We have implemented a prototype of Tupni and evaluated it on ten different formats: five file formats (WMF, BMP, JPG, PNG and TIF) and five network protocols (DNS, RPC, TFTP, HTTP and FTP). Tupni identified all record sequences in the test inputs. We also show that, by aggregating over multiple WMF files, Tupni can derive a more complete format specification for WMF. Furthermore, we demonstrate the utility of Tupni by using the rich information it provides for zero-day vulnerability signature generation, which was not possible with previous reverse engineering tools.

Attackers exploit software vulnerabilities to control or crash programs. Bouncer uses existing software instrumentation techniques to detect attacks and it generates filters automatically to block exploits of the target vulnerabilities. The filters are deployed automatically by instrumenting system calls to drop exploit messages. These filters introduce low overhead and they allow programs to keep running correctly under attack. Previous work computes filters using symbolic execution along the path taken by a sample exploit, but attackers can bypass these filters by generating exploits that follow a different execution path. Bouncer introduces three techniques to generalize filters so that they are harder to bypass: a new form of program slicing that uses a combination of static and dynamic analysis to remove unnecessary conditions from the filter; symbolic summaries for common library functions that characterize their behavior succinctly as a set of conditions on the input; and generation of alternative exploits guided by symbolic execution. Bouncer filters have low overhead, they do not have false positives by design, and our results show that Bouncer can generate filters that block all exploits of some real-world vulnerabilities.

In this paper, we present ShieldGen, a system for automatically generating a data patch or a vulnerability signature for an unknown vulnerability, given a zero-day attack instance. The key novelty in our work is that we leverage knowledge of the data format to generate new potential attack instances, which we call probes, and use a zero-day detector as an oracle to determine if an instance can still exploit the vulnerability; the feedback of the oracle guides our search for the vulnerability signature. We have implemented a ShieldGen prototype and experimented with three known vulnerabilities. The generated signatures have no false positives and a low rate of false negatives due to imperfect data format specifications and the sampling technique used in our probe generation. Overall, they are significantly more precise than the signatures generated by existing schemes. We have also conducted a detailed study of 25 vulnerabilities for which Microsoft has issued security bulletins between 2003 and 2006. We estimate that ShieldGen can produce high quality signatures for a large portion of those vulnerabilities and that the signatures are superior to the signatures generated by existing schemes.

Article Random generation of embedded graphs and an extension to Dobrushin uniqueness (extended abstract) Share on Authors: Marcus Peinado Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany and Internationl Computer Science Institute, Berkeley, California Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany and Internationl Computer Science Institute, Berkeley, CaliforniaView Profile , Thomas Lengauer Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, GermanyView Profile Authors Info & Claims STOC '98: Proceedings of the thirtieth annual ACM symposium on Theory of computingMay 1998 Pages 176–185https://doi.org/10.1145/276698.276729Online:23 May 1998Publication History 1citation235DownloadsMetricsTotal Citations1Total Downloads235Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We develop and experiment with a new parallel algorithm to approximate the maximum weight cut in a weighted undirected graph. Our implementation starts with the recent (serial) algorithm of Goemans and Williamson for this problem. We consider several different versions of this algorithm, varying the interior-point part of the algorithm in order to optimize the parallel efficiency of our method. Our work aims for an efficient, practical formulation of the algorithm with close-to-optimal parallelization. We analyze our parallel algorithm in the LogP model and predict linear speedup for a wide range of the parameters. We have implemented the algorithm using the message passing interface (MPI) and run it on several parallel machines. In particular, we present performance measurements on the IBM SP2, the Connection Machine CM5, and a cluster of workstations. We observe that the measured speedups are predicted well by our analysis in the LogP model. Finally, we test our implementation on several large graphs (up to 13,000 vertices), particularly on large instances of the Ising model.