Making Linux Protection Mechanisms Egalitarian with UserFS

URL to paper on conference site: http://www.usenix.org/events/sec10/tech/UserFS provides egalitarian OS protection mechanisms in Linux. UserFS allows any user—not just the system administrator—to allocate Unix user IDs, to use chroot, and to set up firewall rules in order to confine untrusted code. One key idea in UserFS is representing user IDs as files in a /proc-like file system, thus allowing applications to manage user IDs like any other files, by setting permissions and passing file descriptors over Unix domain sockets. UserFS addresses several challenges in making user IDs egalitarian, including accountability, resource allocation, persistence, and UID reuse. We have ported several applications to take advantage of UserFS; by changing just tens to hundreds of lines of code, we prevented attackers from exploiting application-level vulnerabilities, such as code injection or missing ACL checks in a PHP-based wiki application. Implementing UserFS requires minimal changes to the Linux kernel—a single 3,000-line kernel module—and incurs no performance overhead for most operations, making it practical to deploy on real systems.Quanta Computer (Firm)Samsung Scholarship Foundatio

PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models

Uncertainty learning and quantification of models are crucial tasks to enhance the trustworthiness of the models. Importantly, the recent surge of generative language models (GLMs) emphasizes the need for reliable uncertainty quantification due to the concerns on generating hallucinated facts. In this paper, we propose to learn neural prediction set models that comes with the probably approximately correct (PAC) guarantee for quantifying the uncertainty of GLMs. Unlike existing prediction set models, which are parameterized by a scalar value, we propose to parameterize prediction sets via neural networks, which achieves more precise uncertainty quantification but still satisfies the PAC guarantee. We demonstrate the efficacy of our method on four types of language datasets and six types of models by showing that our method improves the quantified uncertainty by $63\%$ on average, compared to a standard baseline method

Optimizing unit test execution in large software programs using dependency analysis

Tao is a system that optimizes the execution of unit tests in large software programs and reduces the programmer wait time from minutes to seconds. Tao is based on two key ideas: First, Tao focuses on efficiency, unlike past work that focused on avoiding false negatives. Tao implements simple and fast function-level dependency tracking that identifies tests to run on a code change; any false negatives missed by this dependency tracking are caught by running the entire test suite on a test server once the code change is committed. Second, to make it easy for programmers to adopt Tao, it incorporates the dependency information into the source code repository. This paper describes an early prototype of Tao and demonstrates that Tao can reduce unit test execution time in two large Python software projects by over 96% while incurring few false negatives.United States. Defense Advanced Research Projects Agency (DARPA Clean-slate design of Resilient, Adaptive, Secure Hosts (CRASH) program under contract #N66001-10-2-4089)National Science Foundation (U.S.) (NSF award CNS-1053143

Asynchronous intrusion recovery for interconnected web services

Recovering from attacks in an interconnected system is difficult, because an adversary that gains access to one part of the system may propagate to many others, and tracking down and recovering from such an attack requires significant manual effort. Web services are an important example of an interconnected system, as they are increasingly using protocols such as OAuth and REST APIs to integrate with one another. This paper presents Aire, an intrusion recovery system for such web services. Aire addresses several challenges, such as propagating repair across services when some servers may be unavailable, and providing appropriate consistency guarantees when not all servers have been repaired yet. Experimental results show that Aire can recover from four realistic attacks, including one modeled after a recent Facebook OAuth vulnerability; that porting existing applications to Aire requires little effort; and that Aire imposes a 19--30% CPU overhead and 6--9 KB/request storage cost for Askbot, an existing web application.National Science Foundation (U.S.) (NSF award CNS-1053143)United States. Defense Advanced Research Projects Agency (DARPA Clean-slate design of Resilient, Adaptive, Secure Hosts (CRASH) program under contract #N66001-10-2-4089

The widths of quarkonia in quark gluon plasma

Recent lattice calculations showed that the quarkonia will survive beyond the phase transition temperature, and will dissolve at different temperatures depending on the type of the quarkonium. In this work, we calculate the thermal width of the quarkonium at finite temperature before it dissolves into open heavy quarks. The input of the calculation are the parton quarkonium dissociation cross section to NLO in QCD, the quarkonium wave function in a temperature-dependent potential from lattice QCD, and a thermal distribution of partons with thermal masses. We find that for the J/psi, the total thermal width above 1.4 Tc becomes larger than 100 to 250 MeV, depending on the effective thermal masses of the quark and gluon, which we take between 400 to 600 MeV. Such a width corresponds to an effective dissociation cross section by gluons between 1.5 to 3.5 mb and by quarks 1 to 2 mb at 1.4 Tc. However, at similar temperatures, we find a much smaller thermal width and effective cross section for the upsilon.Comment: 7 pages, 13 figures, 2 tables, version to be published in Phys. Rev.

Prime+Retouch: When Cache is Locked and Leaked

Caches on the modern commodity CPUs have become one of the major sources of side-channel leakages and been abused as a new attack vector. To thwart the cache-based side-channel attacks, two types of countermeasures have been proposed: detection-based ones that limit the amount of microarchitectural traces an attacker can leave, and cache prefetching-and-locking techniques that claim to prevent such leakage by disallowing evictions on sensitive data. In this paper, we present the Prime+Retouch attack that completely bypasses these defense schemes by accurately inferring the cache activities with the metadata of the cache replacement policy. Prime+Retouch has three noticeable properties: 1) it incurs no eviction on the victim's data, allowing us to bypass the two known mitigation schemes, 2) it requires minimal synchronization of only one memory access to the attacker's pre-primed cache lines, and 3) it leaks data via non-shared memory, yet because underlying eviction metadata is shared. We demonstrate Prime+Retouch in two architectures: predominant Intel x86 and emerging Apple M1. We elucidate how Prime+Retouch can break the T-table implementation of AES with robust cache side-channel mitigations such as Cloak, under both normal and SGX-protected environments. We also manifest feasibility of the Prime+Retouch attack on the M1 platform imposing more restrictions where the precise measurement tools such as core clock cycle timer and performance counters are inaccessible to the attacker. Furthermore, we first demystify undisclosed cache architecture and its eviction policy of L1 data cache on Apple M1 architecture. We also devise a user-space noise-free cache monitoring tool by repurposing Intel TSX