Google Git
Sign in
llvm / llvm-project / 62c42e29ba43c9d79cd5bd2084b641fbff6a96d5
commit62c42e29ba43c9d79cd5bd2084b641fbff6a96d5[log] [tgz]
authorScott Constable <scott.d.constable@intel.com>Fri Apr 03 13:41:34 2020 -0700
committerCraig Topper <craig.topper@intel.com>Fri Apr 03 13:45:50 2020 -0700
treed7668a760fc83e24cf0fb68547fec4cd25d1ad92
parentba1ffd25c1f7a3564136b13e49fb1a7185561613 [diff]
[X86] Add Support for Load Hardening to Mitigate Load Value Injection (LVI)

After finding all such gadgets in a given function, the pass minimally inserts
LFENCE instructions in such a manner that the following property is satisfied:
for all SOURCE+SINK pairs, all paths in the CFG from SOURCE to SINK contain at
least one LFENCE instruction. The algorithm that implements this minimal
insertion is influenced by an academic paper that minimally inserts memory
fences for high-performance concurrent programs:

http://www.cs.ucr.edu/~lesani/companion/oopsla15/OOPSLA15.pdf

The algorithm implemented in this pass is as follows:

1. Build a condensed CFG (i.e., a GadgetGraph) consisting only of the following components:
  -SOURCE instructions (also includes function arguments)
  -SINK instructions
  -Basic block entry points
  -Basic block terminators
  -LFENCE instructions
2. Analyze the GadgetGraph to determine which SOURCE+SINK pairs (i.e., gadgets) are already mitigated by existing LFENCEs. If all gadgets have been mitigated, go to step 6.
3. Use a heuristic or plugin to approximate minimal LFENCE insertion.
4. Insert one LFENCE along each CFG edge that was cut in step 3.
5. Go to step 2.
6. If any LFENCEs were inserted, return true from runOnFunction() to tell LLVM that the function was modified.

By default, the heuristic used in Step 3 is a greedy heuristic that avoids
inserting LFENCEs into loops unless absolutely necessary. There is also a
CLI option to load a plugin that can provide even better optimization,
inserting fewer fences, while still mitigating all of the LVI gadgets.
The plugin can be found here: https://github.com/intel/lvi-llvm-optimization-plugin,
and a description of the pass's behavior with the plugin can be found here:
https://software.intel.com/security-software-guidance/insights/optimized-mitigation-approach-load-value-injection.

Differential Revision: https://reviews.llvm.org/D75937
2 files changed
tree: d7668a760fc83e24cf0fb68547fec4cd25d1ad92
  1. clang/
  2. clang-tools-extra/
  3. compiler-rt/
  4. debuginfo-tests/
  5. libc/
  6. libclc/
  7. libcxx/
  8. libcxxabi/
  9. libunwind/
  10. lld/
  11. lldb/
  12. llvm/
  13. mlir/
  14. openmp/
  15. parallel-libs/
  16. polly/
  17. pstl/
  18. utils/
  19. .arcconfig
  20. .arclint
  21. .clang-format
  22. .clang-tidy
  23. .git-blame-ignore-revs
  24. .gitignore
  25. CONTRIBUTING.md
  26. README.md
README.md

The LLVM Compiler Infrastructure

This directory and its sub-directories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.

The README briefly describes how to get started with building LLVM. For more information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.

Getting Started with the LLVM System

Taken from https://llvm.org/docs/GettingStarted.html.

Overview

Welcome to the LLVM project!

The LLVM project has multiple components. The core of the project is itself called “LLVM”. This contains all of the tools, libraries, and header files needed to process intermediate representations and converts it into object files. Tools include an assembler, disassembler, bitcode analyzer, and bitcode optimizer. It also contains basic regression tests.

C-like languages use the Clang front end. This component compiles C, C++, Objective C, and Objective C++ code into LLVM bitcode -- and from there into object files, using LLVM.

Other components include: the libc++ C++ standard library, the LLD linker, and more.

Getting the Source Code and Building LLVM

The LLVM Getting Started documentation may be out of date. The Clang Getting Started page might have more accurate information.

This is an example work-flow and configuration to get and build the LLVM source:

  1. Checkout LLVM (including related sub-projects like Clang):

    • git clone https://github.com/llvm/llvm-project.git

    • Or, on windows, git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git

  2. Configure and build LLVM and Clang:

    • cd llvm-project

    • mkdir build

    • cd build

    • cmake -G <generator> [options] ../llvm

      Some common build system generators are:

      • Ninja --- for generating Ninja build files. Most llvm developers use Ninja.
      • Unix Makefiles --- for generating make-compatible parallel makefiles.
      • Visual Studio --- for generating Visual Studio projects and solutions.
      • Xcode --- for generating Xcode projects.

      Some Common options:

      • -DLLVM_ENABLE_PROJECTS='...' --- semicolon-separated list of the LLVM sub-projects you'd like to additionally build. Can include any of: clang, clang-tools-extra, libcxx, libcxxabi, libunwind, lldb, compiler-rt, lld, polly, or debuginfo-tests.

        For example, to build LLVM, Clang, libcxx, and libcxxabi, use -DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi".

      • -DCMAKE_INSTALL_PREFIX=directory --- Specify for directory the full path name of where you want the LLVM tools and libraries to be installed (default /usr/local).

      • -DCMAKE_BUILD_TYPE=type --- Valid options for type are Debug, Release, RelWithDebInfo, and MinSizeRel. Default is Debug.

      • -DLLVM_ENABLE_ASSERTIONS=On --- Compile with assertion checks enabled (default is Yes for Debug builds, No for all other build types).

    • cmake --build . [-- [options] <target>] or your build system specified above directly.

      • The default target (i.e. ninja or make) will build all of LLVM.

      • The check-all target (i.e. ninja check-all) will run the regression tests to ensure everything is in working order.

      • CMake will generate targets for each tool and library, and most LLVM sub-projects generate their own check-<project> target.

      • Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for make, use the option -j NNN, where NNN is the number of parallel jobs, e.g. the number of CPUs you have.

    • For more information see CMake

Consult the Getting Started with LLVM page for detailed information on configuring and compiling LLVM. You can visit Directory Layout to learn about the layout of the source code tree.