Speculative Load Hardening

A Spectre Variant #1 Mitigation Technique

Author: Chandler Carruth - chandlerc@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 paper:

  • 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 when the branch predictor will 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 value2.

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 ...
    leak(*pointer1);
  } else {
    // ... more code ...
    leak(*pointer2);
  }
}

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) {
    // Assuming ?: is implemented using branchless logic...
    predicate_state = !condition ? all_zeros_mask : predicate_state;
    // ... lots of code ...
    //
    // Harden the pointer so it can't be loaded
    pointer1 &= predicate_state;
    leak(*pointer1);
  } else {
    predicate_state = condition ? all_zeros_mask : predicate_state;
    // ... more code ...
    //
    // Alternative: Harden the loaded value
    int value2 = *pointer2 & predicate_state;
    leak(value2);
  }
}

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 is not preventing 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 observation of secret data in the first place.
  • It accumulates the predicate state, protecting even in the face of nested correctly predicted control flows.
  • It passes this predicate state across function boundaries to provide interprocedural protection.
  • When hardening the address of a load, it uses a destructive or non-reversible modification of the address to prevent an attacker from reversing the check using attacker-controlled inputs.
  • 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 using static source code annotations or code vulnerable to a variant #1 style attack.

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 or leaked through other side-channels in non-speculative execution. Code dealing with this, e.g cryptographic routines, already uses constant-time algorithms and code to prevent side-channels. Such code should also scrub registers of secret data following these guidelines.
  • 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 data from 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 protected.

Credit:

  • The core idea of tracing misspeculation through data and marking pointers to block misspeculated loads was developed as part of a HACS 2018 discussion between Chandler Carruth, Paul Kocher, Thomas Pornin, and several other 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 confusion”).
  • 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 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:
    // Assuming ?: is implemented using branchless logic...
    predicate_state = (condition != 0) ? all_zeros_mask : predicate_state;
    // ... lots of code ...
    //
    // Harden the pointer so it can't be loaded
    pointer1 &= predicate_state;
    leak(*pointer1);
    break;

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

    // ...
  }
}

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.

Variant #1.1 and #1.2 attacks: “Bounds Check Bypass Store”

Beyond the core variant #1 attack, there are techniques to extend this attack. The primary technique is known as “Bounds Check Bypass Store” and is discussed in this research paper: https://people.csail.mit.edu/vlk/spectre11.pdf

We will analyze these two variants independently. First, variant #1.1 works by speculatively storing over the return address after a bounds check bypass. This speculative store then ends up being used by the CPU during speculative execution of the return, potentially directing speculative execution to arbitrary gadgets in the binary. Let’s look at an example.

unsigned char local_buffer[4];
unsigned char *untrusted_data_from_caller = ...;
unsigned long untrusted_size_from_caller = ...;
if (untrusted_size_from_caller < sizeof(local_buffer)) {
  // Speculative execution enters here with a too-large size.
  memcpy(local_buffer, untrusted_data_from_caller,
         untrusted_size_from_caller);
  // The stack has now been smashed, writing an attacker-controlled
  // address over the return address.
  minor_processing(local_buffer);
  return;
  // Control will speculate to the attacker-written address.
}

However, this can be mitigated by hardening the load of the return address just like any other load. This is sometimes complicated because x86 for example implicitly loads the return address off the stack. However, the implementation technique below is specifically designed to mitigate this implicit load by using the stack pointer to communicate misspeculation between functions. This additionally causes a misspeculation to have an invalid stack pointer and never be able to read the speculatively stored return address. See the detailed discussion below.

For variant #1.2, the attacker speculatively stores into the vtable or jump table used to implement an indirect call or indirect jump. Because this is speculative, this will often be possible even when these are stored in read-only pages. For example:

class FancyObject : public BaseObject {
public:
  void DoSomething() override;
};
void f(unsigned long attacker_offset, unsigned long attacker_data) {
  FancyObject object = getMyObject();
  unsigned long *arr[4] = getFourDataPointers();
  if (attacker_offset < 4) {
    // We have bypassed the bounds check speculatively.
    unsigned long *data = arr[attacker_offset];
    // Now we have computed a pointer inside of `object`, the vptr.
    *data = attacker_data;
    // The vptr points to the virtual table and we speculatively clobber that.
    g(object); // Hand the object to some other routine.
  }
}
// In another file, we call a method on the object.
void g(BaseObject &object) {
  object.DoSomething();
  // This speculatively calls the address stored over the vtable.
}

Mitigating this requires hardening loads from these locations, or mitigating the indirect call or indirect jump. Any of these are sufficient to block the call or jump from using a speculatively stored value that has been read back.

For both of these, using retpolines would be equally sufficient. One possible hybrid approach is to use retpolines for indirect call and jump, while relying on SLH to mitigate returns.

Another approach that is sufficient for both of these is to harden all of the speculative stores. However, as most stores aren’t interesting and don’t inherently leak data, this is expected to be prohibitively expensive given the attack it is defending against.

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
        retq
.LBB0_3:                                # %danger
        movl    (%rcx), %edi
        callq   leak
        popq    %rax
        retq

When we go to speculatively execute the load, we want to know whether any of the dynamically executed predicates have been misspeculated. 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
.LBB0_1:
        cmoveq  %r8, %rax               # Conditionally update predicate state.
        popq    %rax
        retq
.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 misspeculated, 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

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 (not yet implemented, use retpolines):

        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
        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",@progbits
        .p2align        3
.LJTI0_0:
        .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
return_addr:
        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), we can compute the expected return address prior to the call into a register preserved across the call and use that similarly to the above.

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 those.
    • 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 reloading.

Hardening Loads

Once we have the predicate accumulated into a special value for correct vs. misspeculated, 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 occurring. 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 misspeculated 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 misspeculated paths and an all-one mask along correct paths, or an or instruction with an all-one mask along misspeculated paths and an all-zero mask along correct paths. Other options become less appealing such as multiplying by zero, 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 value.

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 cost.

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 cmoved over %rax in misspeculated 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.
        pushfq
        orq     %rax, %rcx              # Mask the pointer if misspeculating.
        orq     %rax, %rdx              # Mask the index if misspeculating.
        popfq
        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 following:

        ...

.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 misspeculated 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 misspeculated predicate but where the load and the misspeculated 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 misspeculation. The callsite instruction sequence looks like (assuming a misspeculated 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 return:

other_function:
        # prolog
        callq   other_function
        movq    %rsp, %rax
        sarq    63, %rax                # Sign extend the high bit to all bits.
        # ...

.LBB0_N:
        cmovneq %r8, %rax               # Conditionally update predicate state.
        shlq    $47, %rax
        orq     %rax, %rsp
        retq

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 misspeculation in an unmitigated caller.

There is also an advantage to using this form of interprocedural mitigation: by forming these invalid stack pointer addresses we can prevent speculative returns from successfully reading speculatively written values to the actual stack. This works first by forming a data-dependency between computing the address of the return address on the stack and our predicate state. And even when satisfied, if a misprediction causes the state to be poisoned the resulting stack pointer will be invalid.

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

An lfence instruction can be used to prevent subsequent loads from speculatively executing until all prior mispredicted predicates have resolved. We can use this broader barrier to speculative loads executing 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 misspeculation entering a function which is mitigated, regardless of what occurred in the caller. However, such a mixture is inherently more risky. Whether this kind of mixture is a sufficient mitigation requires careful analysis.

Unfortunately, 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, making it an unpleasant tradeoff even when only used in a mixture of code.

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.

High-Level Alternative Mitigation Strategies

There are completely different alternative approaches to mitigating variant 1 attacks. Most discussion so far 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 data. On AMD systems lfence is currently a no-op, but can be made dispatch-serializing by setting an MSR, and thus preclude misspeculation of the code path (mitigation G-2 + V1-1).

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 switch. 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 has shown that this approach 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 helps 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

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 protection.

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 misspeculation 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.