The High-Level Benefits of Low-Level Sandboxing

被引:10
|
作者
Sammler, Michael [1 ,2 ]
Garg, Deepak [1 ]
Dreyer, Derek [1 ]
Litak, Tadeusz [2 ]
机构
[1] MPI SWS, Saarland Informat Campus, Saarbrucken, Germany
[2] Friedrich Alexander Univ Erlangen Nurnberg, Erlangen, Germany
基金
欧洲研究理事会;
关键词
Sandboxing; robust safety; Iris; type systems; logical relations; language-based security; CHECKING;
D O I
10.1145/3371100
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Sandboxing is a common technique that allows low-level, untrusted components to safely interact with trusted code. However, previous work has only investigated the low-level memory isolation guarantees of sandboxing, leaving open the question of the end-to-end guarantees that sandboxing affords programmers. In this paper, we fill this gap by showing that sandboxing enables reasoning about the known concept of robust safety, i.e., safety of the trusted code even in the presence of arbitrary untrusted code. To do this, we first present an idealized operational semantics for a language that combines trusted code with untrusted code. Sandboxing is built into our semantics. Then, we prove that safety properties of the trusted code (as enforced through a rich type system) are upheld in the presence of arbitrary untrusted code, so long as all interactions with untrusted code occur at the "any" type (a type inhabited by all values). Finally, to alleviate the burden of having to interact with untrusted code at only the "any" type, we formalize and prove safe several wrappers, which automatically convert values between the "any" type and much richer types. All our results are mechanized in the Coq proof assistant.
引用
收藏
页数:32
相关论文
共 50 条
  • [21] LOW-LEVEL RADIOACTIVE-WASTES, HIGH-LEVEL RISK
    NEWMAN, A
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 1994, 28 (11) : A488 - A491
  • [22] Music Genre Prediction by Low-Level and High-Level Characteristics
    Vatolkin, Igor
    Roetter, Guenther
    Weihs, Claus
    [J]. DATA ANALYSIS, MACHINE LEARNING AND KNOWLEDGE DISCOVERY, 2014, : 427 - 434
  • [23] High-level soccer indexing on low-level feature space
    Sugano, M
    Uemura, K
    Nakajima, Y
    Yanagihara, H
    [J]. ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5, 2004, : 1625 - 1628
  • [24] Drawing the boundary between low-level and high-level mindreading
    Frédérique de Vignemont
    [J]. Philosophical Studies, 2009, 144 : 457 - 466
  • [25] CBIR: From low-level features to high-level semantics
    Zhou, XS
    Huang, TS
    [J]. IMAGE AND VIDEO COMMUNICATIONS AND PROCESSING 2000, 2000, 3974 : 426 - 431
  • [26] Reconciling High-Level Optimizations and Low-Level Code in LLVM
    Lee, Juneyoung
    Hur, Chung-Kil
    Jung, Ralf
    Liu, Zhengyang
    Regehr, John
    Lopes, Nuno P.
    [J]. PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL, 2018, 2
  • [27] Drawing the boundary between low-level and high-level mindreading
    de Vignemont, Frederique
    [J]. PHILOSOPHICAL STUDIES, 2009, 144 (03) : 457 - 466
  • [28] High-level to Low-level in Unity with GPU Shader Programming
    Hmeljak, Dimitrij
    [J]. PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 2, 2022, : 1140 - 1140
  • [29] Psilocybin impairs high-level but not low-level motion perception
    Carter, OL
    Pettigrew, JD
    Burr, DC
    Alais, D
    Hasler, F
    Vollenweider, FX
    [J]. NEUROREPORT, 2004, 15 (12) : 1947 - 1951
  • [30] Unifying Low-Level and High-Level Music Similarity Measures
    Bogdanov, Dmitry
    Serra, Joan
    Wack, Nicolas
    Herrera, Perfecto
    Serra, Xavier
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2011, 13 (04) : 687 - 701