[llvm-dev] RFC: Speculative Load Hardening (a Spectre variant #1 mitigation)

Kristof Beyls via llvm-dev llvm-dev at lists.llvm.org
Mon Jul 9 05:03:09 PDT 2018

Hi Chandler,

I've just uploaded a sequence of patches that implement a similar technique for
A small difference of approach is that I went for introducing an intrinsic that
can make any integer or pointer value "speculation-safe", i.e. the intrinsic
returns the value of its only parameter when correctly speculating, and returns
0 when miss-speculating.
The intrinsic is close to what Philip Reames suggested on

Then a later patch (D49072) adds automatic mitigation by inserting the intrinsic
in necessary locations.

I believe this approach has the advantage that:
a) it makes it possible to only insert a mitigation in specific locations if
   the programmer is capable of inserting intrinsics manually.
b) it becomes easier to explore different options for implementing automatic
   protection - it's just a matter of writing different ways on how the
   intrinsic is injected into the program. See D49072 for how this is relatively

I've split the patches according to the following functionality:
- LLVM: https://reviews.llvm.org/D49069: Introduce control flow speculation tracking for AArch64.
- LLVM: https://reviews.llvm.org/D49070: Introduce llvm.speculation_safe_value intrinsic.
- LLVM: https://reviews.llvm.org/D49071: Enable lowering of llvm.speculation_safe_value to DSB/ISB pair.
- LLVM: https://reviews.llvm.org/D49072: Enable automatic mitigation against control flow speculation.
- Clang: https://reviews.llvm.org/D49073: Introducing __builtin_speculation_safe_value.

I'll be on a long holiday soon, so there may be delays to me reacting on review feedback.



On 23 Mar 2018, at 11:56, Chandler Carruth via llvm-dev <llvm-dev at lists.llvm.org<mailto:llvm-dev at lists.llvm.org>> wrote:

Hello all,

I've been working for the last month or so on a comprehensive mitigation approach to variant #1 of Spectre. There are a bunch of reasons why this is desirable:
- Critical software that is unlikely to be easily hand-mitigated (or where the performance tradeoff isn't worth it) will have a compelling option.
- It gives us a baseline on performance for hand-mitigation.
- Combined with opt-in or opt-out, it may give simpler hand-mitigation.
- It is instructive to see *how* to mitigate code patterns.

A detailed design document is available for commenting here:
(I pasted this in markdown format at the bottom of the email as well.)

I have also published a very early prototype patch that implements this design:
This is the patch I've used to collect the performance data on the approach. It should be fairly functional but is a long way from being ready to review in detail, much less land. I'm posting so folks can start seeing the overall approach and can play with it if they want. Grab it here:

Comments are very welcome! I'd like to keep the doc and this thread focused on discussion of the high-level technique for hardening, and the code review thread for discussion of the techniques used to implement this in LLVM.

Thanks all!

# Speculative Load Hardening

### A Spectre Variant #1 Mitigation Technique

Author: Chandler Carruth - [chandlerc at google.com<mailto:chandlerc at google.com>](mailto:chandlerc at google.com<mailto:chandlerc at google.com>)

## Problem Statement

Recently, Google Project Zero and other researchers have found information leak
vulnerabilities by exploiting speculative execution in modern CPUs. These
exploits are currently broken down into three variants:
* GPZ Variant #1 (a.k.a. Spectre Variant #1): Bounds check (or predicate) bypass
* GPZ Variant #2 (a.k.a. Spectre Variant #2): Branch target injection
* GPZ Variant #3 (a.k.a. Meltdown): Rogue data cache load

For more details, see the Google Project Zero blog post and the Spectre research
* https://googleprojectzero.blogspot.com/2018/01/reading-privileged-memory-with-side.html
* https://spectreattack.com/spectre.pdf

The core problem of GPZ Variant #1 is that speculative execution uses branch
prediction to select the path of instructions speculatively executed. This path
is speculatively executed with the available data, and may load from memory and
leak the loaded values through various side channels that survive even when the
speculative execution is unwound due to being incorrect. Mispredicted paths can
cause code to be executed with data inputs that never occur in correct
executions, making checks against malicious inputs ineffective and allowing
attackers to use malicious data inputs to leak secret data. Here is an example,
extracted and simplified from the Project Zero paper:
struct array {
  unsigned long length;
  unsigned char data[];
struct array *arr1 = ...; // small array
struct array *arr2 = ...; // array of size 0x400
unsigned long untrusted_offset_from_caller = ...;
if (untrusted_offset_from_caller < arr1->length) {
  unsigned char value = arr1->data[untrusted_offset_from_caller];
  unsigned long index2 = ((value&1)*0x100)+0x200;
  unsigned char value2 = arr2->data[index2];

The key of the attack is to call this with `untrusted_offset_from_caller` that
is far outside of the bounds once the branch predictor is trained to predict
that it will be in-bounds. In that case, the body of the `if` will be executed
speculatively, and may read secret data into `value` and leak it via
a cache-timing side channel when a dependent access is made to populate

## High Level Mitigation Approach

While several approaches are being actively pursued to mitigate specific
branches and/or loads inside especially risky software (most notably various OS
kernels), these approaches require manual and/or static analysis aided auditing
of code and explicit source changes to apply the mitigation. They are unlikely
to scale well to large applications. We are proposing a comprehensive mitigation
approach that would apply automatically across an entire program rather than
through manual changes to the code. While this is likely to have a high
performance cost, some applications may be in a good position to take this
performance / security tradeoff.

The specific technique we propose is to cause loads to be checked using
branchless code to ensure that they are executing along a valid control flow
path. Consider the following C-pseudo-code representing the core idea of
a predicate guarding potentially invalid loads:
void leak(int data);
void example(int* pointer1, int* pointer2) {
  if (condition) {
    // ... lots of code ...
  } else {
    // ... more code ...

This would get transformed into something resembling the following:
uintptr_t all_ones_mask = std::numerical_limits<uintptr_t>::max();
uintptr_t all_zeros_mask = 0;
void leak(int data);
void example(int* pointer1, int* pointer2) {
  uintptr_t predicate_state = all_ones_mask;
  if (condition) {
    predicate_state = !condition ? all_zeros_mask : predicate_state;
    // ... lots of code ...
    // Harden the pointer so it can't be loaded
    pointer1 &= predicate_state;
  } else {
    predicate_state = condition ? all_zeros_mask : predicate_state;
    // ... more code ...
    // Alternative: Harden the loaded value
    int value2 = *pointer2 & predicate_state;

The result should be that if the `if (condition) {` branch is mis-predicted,
there is a *data* dependency on the condition used to zero out any pointers
prior to loading through them or to zero out all of the loaded bits. Even though
this code pattern may still execute speculatively, *invalid* speculative
executions are prevented from leaking secret data from memory (but note that
this data might still be loaded in safe ways, and some regions of memory are
required to not hold secrets, see below for detailed limitations). This approach
only requires the underlying hardware have a way to implement a branchless and
unpredicted conditional update of a register's value. All modern architectures
have support for this, and in fact such support is necessary to correctly
implement constant time cryptographic primitives.

Crucial properties of this approach:
* It does not attempt to prevent any particular side-channel from working. This
  is important as there are an unknown number of potential side channels and we
  expect to continue discovering more. Instead, it prevents the read of secret
  data in the first place.
* It accumulates the predicate state, protecting even in the face of nested
  *correctly* predicted control flows.
* It uses a *destructive* or *non-reversible* modification of the address loaded
  to prevent an attacker from reversing the check using attacker-controlled
  offsets to the pointer.
* It does not completely block speculative execution, and merely prevents
  *mis*-speculated paths from leaking secrets from memory (and stalls
  speculation until this can be determined).
* It is completely general and makes no fundamental assumptions about the
  underlying architecture other than the ability to do branchless conditional
  data updates and a lack of value prediction.
* It does not require programmers to identify all possible secret data or
  information leaks.

Limitations of this approach:
* It requires re-compiling source code to insert hardening instruction
  sequences. Only software compiled in this mode is protected.
* The performance is heavily dependent on a particular architecture's
  implementation strategy. We outline a potential x86 implementation below and
  characterize its performance.
* It does not defend against secret data already loaded from memory and residing
  in registers. Code dealing with this, e.g cryptographic routines, already have
  patterns to scrub registers of secret data, but these patterns must also be
  made unconditional.
* To achieve reasonable performance, many loads may not be checked, such as
  those with compile-time fixed addresses. This primarily consists of accesses
  at compile-time constant offsets of global and local variables. Code which
  needs this protection and intentionally stores secret data must ensure the
  memory regions used for secret data are necessarily dynamic mappings or heap
  allocations. This is an area which can be tuned to provide more comprehensive
  protection at the cost of performance.
* Hardened loads may still load valid addresses if not attacker-controlled
  addresses. To prevent these from reading secret data, the low 2gb of the
  address space and 2gb above and below any executable pages should be

* The core idea of tracing mis-speculation through data and marking pointers to
  block mis-speculated loads was developed as part of a HACS 2018 discussion
  with several individuals.
* Core idea of masking out loaded bits was part of the original mitigation
  suggested by Jann Horn when these attacks were reported.

### Indirect Branches, Calls, and Returns

It is possible to attack control flow other than conditional branches with
variant #1 style mispredictions.
* A prediction towards a hot call target of a virtual method can lead to it
  being speculatively executed when an expected type is used (often called "type
* A hot case may be speculatively executed due to prediction instead of the
  correct case for a switch statement implemented as a jump table.
* A hot common return address may be predicted incorrectly when returning from
  a function.

These code patterns are also vulnerable to Spectre variant #2, and as such are
best mitigated with
a [retpoline](https://support.google.com/faqs/answer/7625886) on x86 platforms.
When a mitigation technique like retpoline is used, speculation simply cannot
proceed through an indirect control flow edge (or it cannot be mispredicted in
the case of a filled RSB) and so it is also protected from variant #1 style
attacks. However, some architectures, micro-architectures, or vendors do not
employ the retpoline mitigation, and on future x86 hardware (both Intel and AMD)
it is expected to become unnecessary due to hardware-based mitigation.

When not using a retpoline, these edges will need independent protection from
variant #1 style attacks. The analogous approach to that used for conditional
control flow should work:
uintptr_t all_ones_mask = std::numerical_limits<uintptr_t>::max();
uintptr_t all_zeros_mask = 0;
void leak(int data);
void example(int* pointer1, int* pointer2) {
  uintptr_t predicate_state = all_ones_mask;
  switch (condition) {
  case 0:
    predicate_state = (condition != 0) ? all_zeros_mask : predicate_state;
    // ... lots of code ...
    // Harden the pointer so it can't be loaded
    pointer1 &= predicate_state;

  case 1:
    predicate_state = (condition != 1) ? all_zeros_mask : predicate_state;
    // ... more code ...
    // Alternative: Harden the loaded value
    int value2 = *pointer2 & predicate_state;

    // ...

The core idea remains the same: validate the control flow using data-flow and
use that validation to check that loads cannot leak information along
misspeculated paths. Typically this involves passing the desired target of such
control flow across the edge and checking that it is correct afterwards. Note
that while it is tempting to think that this mitigates variant #2 attacks, it
does not. Those attacks go to arbitrary gadgets that don't include the checks.

## Implementation Details

There are a number of complex details impacting the implementation of this
technique, both on a particular architecture and within a particular compiler.
We discuss proposed implementation techniques for the x86 architecture and the
LLVM compiler. These are primarily to serve as an example, as other
implementation techniques are very possible.

### x86 Implementation Details

On the x86 platform we break down the implementation into three core components:
accumulating the predicate state through the control flow graph, checking the
loads, and checking control transfers between procedures.

#### Accumulating Predicate State

Consider baseline x86 instructions like the following, which test three
conditions and if all pass, loads data from memory and potentially leaks it
through some side channel:
# %bb.0:                                # %entry
        pushq   %rax
        testl   %edi, %edi
        jne     .LBB0_4
# %bb.1:                                # %then1
        testl   %esi, %esi
        jne     .LBB0_4
# %bb.2:                                # %then2
        testl   %edx, %edx
        je      .LBB0_3
.LBB0_4:                                # %exit
        popq    %rax
.LBB0_3:                                # %danger
        movl    (%rcx), %edi
        callq   leak
        popq    %rax

When we go to speculatively execute the load, we want to know whether any of the
dynamically executed predicates have been mis-speculated. To track that, along
each conditional edge, we need to track the data which would allow that edge to
be taken. On x86, this data is stored in the flags register used by the
conditional jump instruction. Along both edges after this fork in control flow,
the flags register remains alive and contains data that we can use to build up
our accumulated predicate state. We accumulate it using the x86 conditional move
instruction which also reads the flag registers where the state resides. These
conditional move instructions are known to not be predicted on any x86
processors, making them immune to misprediction that could reintroduce the
vulnerability. When we insert the conditional moves, the code ends up looking
like the following:
# %bb.0:                                # %entry
        pushq   %rax
        xorl    %eax, %eax              # Zero out initial predicate state.
        movq    $-1, %r8                # Put all-ones mask into a register.
        testl   %edi, %edi
        jne     .LBB0_1
# %bb.2:                                # %then1
        cmovneq %r8, %rax               # Conditionally update predicate state.
        testl   %esi, %esi
        jne     .LBB0_1
# %bb.3:                                # %then2
        cmovneq %r8, %rax               # Conditionally update predicate state.
        testl   %edx, %edx
        je      .LBB0_4
        cmoveq  %r8, %rax               # Conditionally update predicate state.
        popq    %rax
.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.

Here we create the "empty" or "correct execution" predicate state by zeroing
`%rax`, and we create a constant "incorrect execution" predicate value by
putting `-1` into `%r8`. Then, along each edge coming out of a conditional
branch we do a conditional move that in a correct execution will be a no-op, but
if mis-speculated, will replace the `%rax` with the value of `%r8`.
Misspeculating any one of the three predicates will cause `%rax` to hold the
"incorrect execution" value from `%r8` as we preserve incoming values when
execution is correct rather than overwriting it.

We now have a value in `%rax` in each basic block that indicates if at some
point previously a predicate was mispredicted. And we have arranged for that
value to be particularly effective when used below to harden loads.

##### Indirect Call, Branch, and Return Predicates

(Not yet implemented.)

There is no analogous flag to use when tracing indirect calls, branches, and
returns. The predicate state must be accumulated through some other means.
Fundamentally, this is the reverse of the problem posed in CFI: we need to check
where we came from rather than where we are going. For function-local jump
tables, this is easily arranged by testing the input to the jump table within
each destination:
        pushq   %rax
        xorl    %eax, %eax              # Zero out initial predicate state.
        movq    $-1, %r8                # Put all-ones mask into a register.
        jmpq    *.LJTI0_0(,%rdi,8)      # Indirect jump through table.
.LBB0_2:                                # %sw.bb<http://sw.bb/>
        testq   $0, %rdi                # Validate index used for jump table.
        cmovneq %r8, %rax               # Conditionally update predicate state.
        jmp     _Z4leaki                # TAILCALL

.LBB0_3:                                # %sw.bb1
        testq   $1, %rdi                # Validate index used for jump table.
        cmovneq %r8, %rax               # Conditionally update predicate state.
        jmp     _Z4leaki                # TAILCALL

.LBB0_5:                                # %sw.bb10
        testq   $2, %rdi                # Validate index used for jump table.
        cmovneq %r8, %rax               # Conditionally update predicate state.
        jmp     _Z4leaki                # TAILCALL

        .section        .rodata,"a", at progbits
        .p2align        3
        .quad   .LBB0_2
        .quad   .LBB0_3
        .quad   .LBB0_5

Returns have a simple mitigation technique on x86-64 (or other ABIs which have
what is called a "red zone" region beyond the end of the stack). This region is
guaranteed to be preserved across interrupts and context switches, making the
return address used in returning to the current code remain on the stack and
valid to read. We can emit code in the caller to verify that a return edge was
not mispredicted:
        callq   other_function
        testq   -8(%rsp), return_addr   # Validate return address.
        cmovneq %r8, %rax               # Update predicate state.

For an ABI without a "red zone" (and thus unable to read the return address from
the stack), mitigating returns face similar problems to calls below.

Indirect calls (and returns in the absence of a red zone ABI) pose the most
significant challenge to propagate. The simplest technique would be to define
a new ABI such that the intended call target is passed into the called function
and checked in the entry. Unfortunately, new ABIs are quite expensive to deploy
in C and C++. While the target function could be passed in TLS, we would still
require complex logic to handle a mixture of functions compiled with and without
this extra logic (essentially, making the ABI backwards compatible). Currently,
we suggest using retpolines here and will continue to investigate ways of
mitigating this.

##### Optimizations, Alternatives, and Tradeoffs

Merely accumulating predicate state involves significant cost. There are several
key optimizations we employ to minimize this and various alternatives that
present different tradeoffs in the generated code.

First, we work to reduce the number of instructions used to track the state:
* Rather than inserting a `cmovCC` instruction along every conditional edge in
  the original program, we track each set of condition flags we need to capture
  prior to entering each basic block and reuse a common `cmovCC` sequence for
  * We could further reuse suffixes when there are multiple `cmovCC`
    instructions required to capture the set of flags. Currently this is
    believed to not be worth the cost as paired flags are relatively rare and
    suffixes of them are exceedingly rare.
* A common pattern in x86 is to have multiple conditional jump instructions that
  use the same flags but handle different conditions. Naively, we could consider
  each fallthrough between them an "edge" but this causes a much more complex
  control flow graph. Instead, we accumulate the set of conditions necessary for
  fallthrough and use a sequence of `cmovCC` instructions in a single
  fallthrough edge to track it.

Second, we trade register pressure for simpler `cmovCC` instructions by
allocating a register for the "bad" state. We could read that value from memory
as part of the conditional move instruction, however, this creates more
micro-ops and requires the load-store unit to be involved. Currently, we place
the value into a virtual register and allow the register allocator to decide
when the register pressure is sufficient to make it worth spilling to memory and

#### Hardening Loads

Once we have the predicate accumulated into a special value for correct vs.
mis-speculated, we need to apply this to loads in a way that ensures they do not
leak secret data. There are two primary techniques for this: we can either
harden the loaded value to prevent observation, or we can harden the address
itself to prevent the load from occuring. These have significantly different
performance tradeoffs.

##### Hardening loaded values

The most appealing way to harden loads is to mask out all of the bits loaded.
The key requirement is that for each bit loaded, along the mis-speculated path
that bit is always fixed at either 0 or 1 regardless of the value of the bit
loaded. The most obvious implementation uses either an `and` instruction with an
all-zero mask along mis-speculated paths and an all-one mask along correct
paths, or an `or` instruction with an all-one mask along mis-speculated paths
and an all-zero mask along correct paths. Other options become less appealing
such as multiplying by zero or one, or multiple shift instructions. For reasons
we elaborate on below, we end up suggesting you use `or` with an all-ones mask,
making the x86 instruction sequence look like the following:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        movl    (%rsi), %edi            # Load potentially secret data from %rsi.
        orl     %eax, %edi

Other useful patterns may be to fold the load into the `or` instruction itself
at the cost of a register-to-register copy.

There are some challenges with deploying this approach:
1. Many loads on x86 are folded into other instructions. Separating them would
   add very significant and costly register pressure with prohibitive
   performance cost.
2. Loads may not target a general purpose register requiring extra instructions
   to map the state value into the correct register class, and potentially more
   expensive instructions to mask the value in some way.
3. The flags registers on x86 are very likely to be live, and challenging to
   preserve cheaply.
4. There are many more values loaded than pointers & indices used for loads. As
   a consequence, hardening the result of a load requires substantially more
   instructions than hardening the address of the load (see below).

Despite these challenges, hardening the result of the load critically allows
the load to proceed and thus has dramatically less impact on the total
speculative / out-of-order potential of the execution. There are also several
interesting techniques to try and mitigate these challenges and make hardening
the results of loads viable in at least some cases. However, we generally
expect to fall back when unprofitable from hardening the loaded value to the
next approach of hardening the address itself.

###### Loads folded into data-invariant operations can be hardened after the operation

The first key to making this feasible is to recognize that many operations on
x86 are "data-invariant". That is, they have no (known) observable behavior
differences due to the particular input data. These instructions are often used
when implementing cryptographic primitives dealing with private key data
because they are not believed to provide any side-channels. Similarly, we can
defer hardening until after them as they will not in-and-of-themselves
introduce a speculative execution side-channel. This results in code sequences
that look like:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        addl    (%rsi), %edi            # Load and accumulate without leaking.
        orl     %eax, %edi

While an addition happens to the loaded (potentially secret) value, that
doesn't leak any data and we then immediately harden it.

###### Hardening of loaded values deferred down the data-invariant expression graph

We can generalize the previous idea and sink the hardening down the expression
graph across as many data-invariant operations as desirable. This can use very
conservative rules for whether something is data-invariant. The primary goal
should be to handle multiple loads with a single hardening instruction:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        addl    (%rsi), %edi            # Load and accumulate without leaking.
        addl    4(%rsi), %edi           # Continue without leaking.
        addl    8(%rsi), %edi
        orl     %eax, %edi              # Mask out bits from all three loads.

###### Preserving the flags while hardening loaded values on Haswell, Zen, and newer processors

Sadly, there are no useful instructions on x86 that apply a mask to all 64 bits
without touching the flag registers. However, we can harden loaded values that
are narrower than a word (fewer than 32-bits on 32-bit systems and fewer than
64-bits on 64-bit systems) by zero-extending the value to the full word size
and then shifting right by at least the number of original bits using the BMI2
`shrx` instruction:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        addl    (%rsi), %edi            # Load and accumulate 32 bits of data.
        shrxq   %rax, %rdi, %rdi        # Shift out all 32 bits loaded.

Because on x86 the zero-extend is free, this can efficiently harden the loaded

##### Hardening the address of the load

When hardening the loaded value is inapplicable, most often because the
instruction directly leaks information (like `cmp` or `jmpq`), we switch to
hardening the _address_ of the load instead of the loaded value. This avoids
increasing register pressure by unfolding the load or paying some other high

To understand how this works in practice, we need to examine the exact
semantics of the x86 addressing modes which, in its fully general form, looks
like `(%base,%index,scale)offset`. Here `%base` and `%index` are 64-bit
registers that can potentially be any value, and may be attacker controlled,
and `scale` and `offset` are fixed immediate values. `scale` must be `1`, `2`,
`4`, or `8`, and `offset` can be any 32-bit sign extended value. The exact
computation performed to find the address is then: `%base + (scale * %index) +
offset` under 64-bit 2's complement modular arithmetic.

One issue with this approach is that, after hardening, the  `%base + (scale *
%index)` subexpression will compute a value near zero (`-1 + (scale * -1)`) and
then a large, positive `offset` will index into memory within the first two
gigabytes of address space. While these offsets are not attacker controlled,
the attacker could chose to attack a load which happens to have the desired
offset and then successfully read memory in that region. This significantly
raises the burden on the attacker and limits the scope of attack but does not
eliminate it. To fully close the attack we must work with the operating system
to preclude mapping memory in the low two gigabytes of address space.

###### 64-bit load checking instructions

We can use the following instruction sequences to check loads. We set up `%r8`
in these examples to hold the special value of `-1` which will be `cmov`ed over
`%rax` in mis-speculated paths.

Single register addressing mode:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        orq     %rax, %rsi              # Mask the pointer if misspeculating.
        movl    (%rsi), %edi

Two register addressing mode:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        orq     %rax, %rsi              # Mask the pointer if misspeculating.
        orq     %rax, %rcx              # Mask the index if misspeculating.
        movl    (%rsi,%rcx), %edi

This will result in a negative address near zero or in `offset` wrapping the
address space back to a small positive address. Small, negative addresses will
fault in user-mode for most operating systems, but targets which need the high
address space to be user accessible may need to adjust the exact sequence used
above. Additionally, the low addresses will need to be marked unreadable by the
OS to fully harden the load.

###### RIP-relative addressing is even easier to break

There is a common addressing mode idiom that is substantially harder to check:
addressing relative to the instruction pointer. We cannot change the value of
the instruction pointer register and so we have the harder problem of forcing
`%base + scale * %index + offset` to be an invalid address, by *only* changing
`%index`. The only advantage we have is that the attacker also cannot modify
`%base`. If we use the fast instruction sequence above, but only apply it to
the index, we will always access `%rip + (scale * -1) + offset`. If the
attacker can find a load which with this address happens to point to secret
data, then they can reach it. However, the loader and base libraries can also
simply refuse to map the heap, data segments, or stack within 2gb of any of the
text in the program, much like it can reserve the low 2gb of address space.

###### The flag registers again make everything hard

Unfortunately, the technique of using `orq`-instructions has a serious flaw on
x86. The very thing that makes it easy to accumulate state, the flag registers
containing predicates, causes serious problems here because they may be alive
and used by the loading instruction or subsequent instructions. On x86, the
`orq` instruction **sets** the flags and will override anything already there.
This makes inserting them into the instruction stream very hazardous.
Unfortunately, unlike when hardening the loaded value, we have no fallback here
and so we must have a fully general approach available.

The first thing we must do when generating these sequences is try to analyze
the surrounding code to prove that the flags are not in fact alive or being
used. Typically, it has been set by some other instruction which just happens
to set the flags register (much like ours!) with no actual dependency. In those
cases, it is safe to directly insert these instructions. Alternatively we may
be able to move them earlier to avoid clobbering the used value.

However, this may ultimately be impossible. In that case, we need to preserve
the flags around these instructions:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        orq     %rax, %rcx              # Mask the pointer if misspeculating.
        orq     %rax, %rdx              # Mask the index if misspeculating.
        movl    (%rcx,%rdx), %edi

Using the `pushf` and `popf` instructions saves the flags register around our
inserted code, but comes at a high cost. First, we must store the flags to the
stack and reload them. Second, this causes the stack pointer to be adjusted
dynamically, requiring a frame pointer be used for referring to temporaries
spilled to the stack, etc.

On newer x86 processors we can use the `lahf` and `sahf` instructions to save
all of the flags besides the overflow flag in a register rather than on the
stack. We can then use `seto` and `add` to save and restore the overflow flag
in a register. Combined, this will save and restore flags in the same manner as
above but using two registers rather than the stack. That is still very
expensive if slightly less expensive than `pushf` and `popf` in most cases.

###### A flag-less alternative on Haswell, Zen and newer processors

Starting with the BMI2 x86 instruction set extensions available on Haswell and
Zen processors, there is an instruction for shifting that does not set any
flags: `shrx`. We can use this and the `lea` instruction to implement analogous
code sequences to the above ones. However, these are still very marginally
slower, as there are fewer ports able to dispatch shift instructions in most
modern x86 processors than there are for `or` instructions.

Fast, single register addressing mode:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        shrxq   %rax, %rsi, %rsi        # Shift away bits if misspeculating.
        movl    (%rsi), %edi

This will collapse the register to zero or one, and everything but the offset
in the addressing mode to be less than or equal to 9. This means the full
address can only be guaranteed to be less than `(1 << 31) + 9`. The OS may wish
to protect an extra page of the low address space to account for this

##### Optimizations

A very large portion of the cost for this approach comes from checking loads in
this way, so it is important to work to optimize this. However, beyond making
the instruction sequences to *apply* the checks efficient (for example by
avoiding `pushfq` and `popfq` sequences), the only significant optimization is
to check fewer loads without introducing a vulnerability. We apply several
techniques to accomplish that.

###### Don't check loads from compile-time constant stack offsets

We implement this optimization on x86 by skipping the checking of loads which
use a fixed frame pointer offset.

The result of this optimization is that patterns like reloading a spilled
register or accessing a global field don't get checked. This is a very
significant performance win.

###### Don't check dependent loads

A core part of why this mitigation strategy works is that it establishes a
data-flow check on the loaded address. However, this means that if the address
itself was already loaded using a checked load, there is no need to check a
dependent load provided it is within the same basic block as the checked load,
and therefore has no additional predicates guarding it. Consider code like the

.LBB0_4:                                # %danger
        movq    (%rcx), %rdi
        movl    (%rdi), %edx

This will get transformed into:

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        orq     %rax, %rcx              # Mask the pointer if misspeculating.
        movq    (%rcx), %rdi            # Hardened load.
        movl    (%rdi), %edx            # Unhardened load due to dependent addr.

This doesn't check the load through `%rdi` as that pointer is dependent on a
checked load already.

###### Protect large, load-heavy blocks with a single lfence

It may be worth using a single `lfence` instruction at the start of a block
which begins with a (very) large number of loads that require independent
protection *and* which require hardening the address of the load. However, this
is unlikely to be profitable in practice. The latency hit of the hardening
would need to exceed that of an `lfence` when *correctly* speculatively
executed. But in that case, the `lfence` cost is a complete loss of speculative
execution (at a minimum). So far, the evidence we have of the performance cost
of using `lfence` indicates few if any hot code patterns where this trade off
would make sense.

###### Tempting optimizations that break the security model

Several optimizations were considered which didn't pan out due to failure to
uphold the security model. One in particular is worth discussing as many others
will reduce to it.

We wondered whether only the *first* load in a basic block could be checked. If
the check works as intended, it forms an invalid pointer that doesn't even
virtual-address translate in the hardware. It should fault very early on in its
processing. Maybe that would stop things in time for the mis-speculated path to
fail to leak any secrets. This doesn't end up working because the processor is
fundamentally out-of-order, even in its speculative domain. As a consequence,
the attacker could cause the initial address computation itself to stall and
allow an arbitrary number of unrelated loads (including attacked loads of
secret data) to pass through.

#### Interprocedural Checking

Modern x86 processors may speculate into called functions and out of functions
to their return address. As a consequence, we need a way to check loads that
occur after a mis-speculated predicate but where the load and the
mis-speculated predicate are in different functions. In essence, we need some
interprocedural generalization of the predicate state tracking. A primary
challenge to passing the predicate state between functions is that we would
like to not require a change to the ABI or calling convention in order to make
this mitigation more deployable, and further would like code mitigated in this
way to be easily mixed with code not mitigated in this way and without
completely losing the value of the mitigation.

##### Embed the predicate state into the high bit(s) of the stack pointer

We can use the same technique that allows hardening pointers to pass the
predicate state into and out of functions. The stack pointer is trivially
passed between functions and we can test for it having the high bits set to
detect when it has been marked due to mis-speculation. The callsite instruction
sequence looks like (assuming a mis-speculated state value of `-1`):

.LBB0_4:                                # %danger
        cmovneq %r8, %rax               # Conditionally update predicate state.
        shlq    $47, %rax
        orq     %rax, %rsp
        callq   other_function
        movq    %rsp, %rax
        sarq    63, %rax                # Sign extend the high bit to all bits.

This first puts the predicate state into the high bits of `%rsp` before calling
the function and then reads it back out of high bits of `%rsp` afterward. When
correctly executing (speculatively or not), these are all no-ops. When
misspeculating, the stack pointer will end up negative. We arrange for it to
remain a canonical address, but otherwise leave the low bits alone to allow
stack adjustments to proceed normally without disrupting this. Within the
called function, we can extract this predicate state and then reset it on
        # prolog
        callq   other_function
        movq    %rsp, %rax
        sarq    63, %rax                # Sign extend the high bit to all bits.
        # ...

        cmovneq %r8, %rax               # Conditionally update predicate state.
        shlq    $47, %rax
        orq     %rax, %rsp

This approach is effective when all code is mitigated in this fashion, and can
even survive very limited reaches into unmitigated code (the state will
round-trip in and back out of an unmitigated function, it just won't be
updated). But it does have some limitations. There is a cost to merging the
state into `%rsp` and it doesn't insulate mitigated code from mis-speculation
in an unmitigated caller.

##### Rewrite API of internal functions to directly propagate predicate state

(Not yet implemented.)

We have the option with internal functions to directly adjust their API to
accept the predicate as an argument and return it. This is likely to be
marginally cheaper than embedding into `%rsp` for entering functions.

##### Use `lfence` to guard function transitions

We know that an `lfence` instruction can be used to block speculation
completely and so we can use this stronger mitigation between functions. We
emit it in the entry block to handle calls, and prior to each return. This
approach also has the advantage of providing the strongest degree of mitigation
when mixed with unmitigated code by halting all mis-speculation entering a
function which is mitigated, regardless of what occured in the caller.

However, experimental results indicate that the performance overhead of this
approach is very high for certain patterns of code. A classic example is any
form of recursive evaluation engine. The hot, rapid call and return sequences
exhibit dramatic performance loss when mitigated with `lfence`. This component
alone can regress performance by 2x or more.

##### Use an internal TLS location to pass predicate state

We can define a special thread-local value to hold the predicate state between
functions. This avoids direct ABI implications by using a side channel between
callers and callees to communicate the predicate state. It also allows implicit
zero-initialization of the state, which allows non-checked code to be the first
code executed.

However, this requires a load from TLS in the entry block, a store to TLS
before every call and every ret, and a load from TLS after every call. As a
consequence it is expected to be substantially more expensive even than using
`%rsp` and potentially `lfence` within the function entry block.

##### Define a new ABI and/or calling convention

We could define a new ABI and/or calling convention to explicitly pass the
predicate state in and out of functions. This may be interesting if none of the
alternatives have adequate performance, but it makes deployment and adoption
dramatically more complex, and potentially infeasible.

## Alternative Mitigation Strategies

[discussion](https://lwn.net/Articles/744287/) on mitigating variant 1 attacks
focuses on mitigating specific known attackable components in the Linux kernel
(or other kernels) by manually rewriting the code to contain an instruction
sequence that is not vulnerable. For x86 systems this is done by either
injecting an `lfence` instruction along the code path which would leak data if
executed speculatively or by rewriting memory accesses to have branch-less
masking to a known safe region. On Intel systems, `lfence` [will prevent the
speculative load of secret
On AMD systems `lfence` is currently a no-op, but can be made
dispatch-serializing by setting an MSR, and thus preclude mis-speculation of
the code path ([mitigation G-2 +

However, this relies on finding and enumerating all possible points in code
which could be attacked to leak information. While in some cases static
analysis is effective at doing this at scale, in many cases it still relies on
human judgement to evaluate whether code might be vulnerable. Especially for
software systems which receive less detailed scrutiny but remain sensitive to
these attacks, this seems like an impractical security model. We need an
automatic and systematic mitigation strategy.

### Automatic `lfence` on Conditional Edges

A natural way to scale up the existing hand-coded mitigations is simply to
inject an `lfence` instruction into both the target and fallthrough
destinations of every conditional branch. This ensures that no predicate or
bounds check can be bypassed speculatively. However, the performance overhead
of this approach is, simply put, catastrophic. Yet it remains the only truly
"secure by default" approach known prior to this effort and serves as the
baseline for performance.

One attempt to address the performance overhead of this and make it more
realistic to deploy is [MSVC's /Qspectre
Their technique is to use static analysis within the compiler to only insert
`lfence` instructions into conditional edges at risk of attack. However,
initial analysis (still under embargo) has shown that this analysis is
incomplete and only catches a small and limited subset of attackable patterns
which happen to resemble very closely the initial proofs of concept. As such,
while its performance is acceptable, it does not appear to be an adequate
systematic mitigation.

## Performance Overhead

The performance overhead of this style of comprehensive mitigation is very
high. However, it compares very favorably with previously recommended
approaches such as the `lfence` instruction. Just as users can restrict the
scope of `lfence` to control its performance impact, this mitigation technique
could be restricted in scope as well.

However, it is important to understand what it would cost to get a fully
mitigated baseline. Here we assume targeting a Haswell (or newer) processor and
using all of the tricks to improve performance (so leaves the low 2gb
unprotected and +/- 2gb surrounding any PC in the program). We ran both
Google's microbenchmark suite and a large highly-tuned server built using
ThinLTO and PGO. All were built with `-march=haswell` to give access to BMI2
instructions, and benchmarks were run on large Haswell servers. We collected
data both with an `lfence`-based mitigation and load hardening as presented
here. The summary is that mitigating with load hardening is 1.77x faster than
mitigating with `lfence`, and the overhead of load hardening compared to a
normal program is likely between a 10% overhead and a 50% overhead with most
large applications seeing a 30% overhead or less.

| Benchmark                              | `lfence` | Load Hardening | Mitigated Speedup |
| -------------------------------------- | -------: | -------------: | ----------------: |
| Google microbenchmark suite            |   -74.8% |         -36.4% |          **2.5x** |
| Large server QPS (using ThinLTO & PGO) |   -62%   |         -29%   |          **1.8x** |

Below is a visualization of the microbenchmark suite results which hels show the
distribution of results that is somewhat lost in the summary. The y-axis is
a log-scale speedup ratio of load hardening relative to `lfence` (up -> faster
-> better). Each box-and-whiskers represents one microbenchmark which may have
many different metrics measured. The red line marks the median, the box marks
the first and third quartiles, and the whiskers mark the min and max.

![Microbenchmark result visualization](speculative_load_hardening_microbenchmarks.png)

We don't yet have benchmark data on SPEC or the LLVM test suite, but we can work
on getting that. Still, the above should give a pretty clear characterization of
the performance, and specific benchmarks are unlikely to reveal especially
interesting properties.

### Future Work: Fine Grained Control and API-Integration

The performance overhead of this technique is likely to be very significant and
something users wish to control or reduce. There are interesting options here
that impact the implementation strategy used.

One particularly appealing option is to allow both opt-in and opt-out of this
mitigation at reasonably fine granularity such as on a per-function basis,
including intelligent handling of inlining decisions -- protected code can be
prevented from inlining into unprotected code, and unprotected code will become
protected when inlined into protected code. For systems where only a limited set
of code is reachable by externally controlled inputs, it may be possible to
limit the scope of mitigation through such mechanisms without compromising the
application's overall security. The performance impact may also be focused in
a few key functions that can be hand-mitigated in ways that have lower
performance overhead while the remainder of the application receives automatic

For both limiting the scope of mitigation or manually mitigating hot functions,
there needs to be some support for mixing mitigated and unmitigated code without
completely defeating the mitigation. For the first use case, it would be
particularly desirable that mitigated code remains safe when being called during
mis-speculation from unmitigated code.

For the second use case, it may be important to connect the automatic mitigation
technique to explicit mitigation APIs such as what is described in
http://wg21.link/p0928 (or any other eventual API) so that there is a clean way
to switch from automatic to manual mitigation without immediately exposing
a hole. However, the design for how to do this is hard to come up with until the
APIs are better established. We will revisit this as those APIs mature.
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