p71924506
/
aosp12
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
aosp12/external/llvm-project/compiler-rt/lib/fuzzer/FuzzerCorpus.h

582 lines
19 KiB
C++
Raw Permalink Blame History

//===- FuzzerCorpus.h - Internal header for the Fuzzer ----------*- C++ -* ===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
// fuzzer::InputCorpus
//===----------------------------------------------------------------------===//
#ifndef LLVM_FUZZER_CORPUS
#define LLVM_FUZZER_CORPUS
#include "FuzzerDataFlowTrace.h"
#include "FuzzerDefs.h"
#include "FuzzerIO.h"
#include "FuzzerRandom.h"
#include "FuzzerSHA1.h"
#include "FuzzerTracePC.h"
#include <algorithm>
#include <chrono>
#include <numeric>
#include <random>
#include <unordered_set>
namespace fuzzer {
struct InputInfo {
Unit U; // The actual input data.
std::chrono::microseconds TimeOfUnit;
uint8_t Sha1[kSHA1NumBytes]; // Checksum.
// Number of features that this input has and no smaller input has.
size_t NumFeatures = 0;
size_t Tmp = 0; // Used by ValidateFeatureSet.
// Stats.
size_t NumExecutedMutations = 0;
size_t NumSuccessfullMutations = 0;
bool NeverReduce = false;
bool MayDeleteFile = false;
bool Reduced = false;
bool HasFocusFunction = false;
Vector<uint32_t> UniqFeatureSet;
Vector<uint8_t> DataFlowTraceForFocusFunction;
// Power schedule.
bool NeedsEnergyUpdate = false;
double Energy = 0.0;
size_t SumIncidence = 0;
Vector<std::pair<uint32_t, uint16_t>> FeatureFreqs;
// Delete feature Idx and its frequency from FeatureFreqs.
bool DeleteFeatureFreq(uint32_t Idx) {
if (FeatureFreqs.empty())
return false;
// Binary search over local feature frequencies sorted by index.
auto Lower = std::lower_bound(FeatureFreqs.begin(), FeatureFreqs.end(),
std::pair<uint32_t, uint16_t>(Idx, 0));
if (Lower != FeatureFreqs.end() && Lower->first == Idx) {
FeatureFreqs.erase(Lower);
return true;
}
return false;
}
// Assign more energy to a high-entropy seed, i.e., that reveals more
// information about the globally rare features in the neighborhood of the
// seed. Since we do not know the entropy of a seed that has never been
// executed we assign fresh seeds maximum entropy and let II->Energy approach
// the true entropy from above. If ScalePerExecTime is true, the computed
// entropy is scaled based on how fast this input executes compared to the
// average execution time of inputs. The faster an input executes, the more
// energy gets assigned to the input.
void UpdateEnergy(size_t GlobalNumberOfFeatures, bool ScalePerExecTime,
std::chrono::microseconds AverageUnitExecutionTime) {
Energy = 0.0;
SumIncidence = 0;
// Apply add-one smoothing to locally discovered features.
for (auto F : FeatureFreqs) {
size_t LocalIncidence = F.second + 1;
Energy -= LocalIncidence * logl(LocalIncidence);
SumIncidence += LocalIncidence;
}
// Apply add-one smoothing to locally undiscovered features.
// PreciseEnergy -= 0; // since logl(1.0) == 0)
SumIncidence += (GlobalNumberOfFeatures - FeatureFreqs.size());
// Add a single locally abundant feature apply add-one smoothing.
size_t AbdIncidence = NumExecutedMutations + 1;
Energy -= AbdIncidence * logl(AbdIncidence);
SumIncidence += AbdIncidence;
// Normalize.
if (SumIncidence != 0)
Energy = (Energy / SumIncidence) + logl(SumIncidence);
if (ScalePerExecTime) {
// Scaling to favor inputs with lower execution time.
uint32_t PerfScore = 100;
if (TimeOfUnit.count() > AverageUnitExecutionTime.count() * 10)
PerfScore = 10;
else if (TimeOfUnit.count() > AverageUnitExecutionTime.count() * 4)
PerfScore = 25;
else if (TimeOfUnit.count() > AverageUnitExecutionTime.count() * 2)
PerfScore = 50;
else if (TimeOfUnit.count() * 3 > AverageUnitExecutionTime.count() * 4)
PerfScore = 75;
else if (TimeOfUnit.count() * 4 < AverageUnitExecutionTime.count())
PerfScore = 300;
else if (TimeOfUnit.count() * 3 < AverageUnitExecutionTime.count())
PerfScore = 200;
else if (TimeOfUnit.count() * 2 < AverageUnitExecutionTime.count())
PerfScore = 150;
Energy *= PerfScore;
}
}
// Increment the frequency of the feature Idx.
void UpdateFeatureFrequency(uint32_t Idx) {
NeedsEnergyUpdate = true;
// The local feature frequencies is an ordered vector of pairs.
// If there are no local feature frequencies, push_back preserves order.
// Set the feature frequency for feature Idx32 to 1.
if (FeatureFreqs.empty()) {
FeatureFreqs.push_back(std::pair<uint32_t, uint16_t>(Idx, 1));
return;
}
// Binary search over local feature frequencies sorted by index.
auto Lower = std::lower_bound(FeatureFreqs.begin(), FeatureFreqs.end(),
std::pair<uint32_t, uint16_t>(Idx, 0));
// If feature Idx32 already exists, increment its frequency.
// Otherwise, insert a new pair right after the next lower index.
if (Lower != FeatureFreqs.end() && Lower->first == Idx) {
Lower->second++;
} else {
FeatureFreqs.insert(Lower, std::pair<uint32_t, uint16_t>(Idx, 1));
}
}
};
struct EntropicOptions {
bool Enabled;
size_t NumberOfRarestFeatures;
size_t FeatureFrequencyThreshold;
bool ScalePerExecTime;
};
class InputCorpus {
static const uint32_t kFeatureSetSize = 1 << 21;
static const uint8_t kMaxMutationFactor = 20;
static const size_t kSparseEnergyUpdates = 100;
size_t NumExecutedMutations = 0;
EntropicOptions Entropic;
public:
InputCorpus(const std::string &OutputCorpus, EntropicOptions Entropic)
: Entropic(Entropic), OutputCorpus(OutputCorpus) {
memset(InputSizesPerFeature, 0, sizeof(InputSizesPerFeature));
memset(SmallestElementPerFeature, 0, sizeof(SmallestElementPerFeature));
}
~InputCorpus() {
for (auto II : Inputs)
delete II;
}
size_t size() const { return Inputs.size(); }
size_t SizeInBytes() const {
size_t Res = 0;
for (auto II : Inputs)
Res += II->U.size();
return Res;
}
size_t NumActiveUnits() const {
size_t Res = 0;
for (auto II : Inputs)
Res += !II->U.empty();
return Res;
}
size_t MaxInputSize() const {
size_t Res = 0;
for (auto II : Inputs)
Res = std::max(Res, II->U.size());
return Res;
}
void IncrementNumExecutedMutations() { NumExecutedMutations++; }
size_t NumInputsThatTouchFocusFunction() {
return std::count_if(Inputs.begin(), Inputs.end(), [](const InputInfo *II) {
return II->HasFocusFunction;
});
}
size_t NumInputsWithDataFlowTrace() {
return std::count_if(Inputs.begin(), Inputs.end(), [](const InputInfo *II) {
return !II->DataFlowTraceForFocusFunction.empty();
});
}
bool empty() const { return Inputs.empty(); }
const Unit &operator[] (size_t Idx) const { return Inputs[Idx]->U; }
InputInfo *AddToCorpus(const Unit &U, size_t NumFeatures, bool MayDeleteFile,
bool HasFocusFunction, bool NeverReduce,
std::chrono::microseconds TimeOfUnit,
const Vector<uint32_t> &FeatureSet,
const DataFlowTrace &DFT, const InputInfo *BaseII) {
assert(!U.empty());
if (FeatureDebug)
Printf("ADD_TO_CORPUS %zd NF %zd\n", Inputs.size(), NumFeatures);
Inputs.push_back(new InputInfo());
InputInfo &II = *Inputs.back();
II.U = U;
II.NumFeatures = NumFeatures;
II.NeverReduce = NeverReduce;
II.TimeOfUnit = TimeOfUnit;
II.MayDeleteFile = MayDeleteFile;
II.UniqFeatureSet = FeatureSet;
II.HasFocusFunction = HasFocusFunction;
// Assign maximal energy to the new seed.
II.Energy = RareFeatures.empty() ? 1.0 : log(RareFeatures.size());
II.SumIncidence = RareFeatures.size();
II.NeedsEnergyUpdate = false;
std::sort(II.UniqFeatureSet.begin(), II.UniqFeatureSet.end());
ComputeSHA1(U.data(), U.size(), II.Sha1);
auto Sha1Str = Sha1ToString(II.Sha1);
Hashes.insert(Sha1Str);
if (HasFocusFunction)
if (auto V = DFT.Get(Sha1Str))
II.DataFlowTraceForFocusFunction = *V;
// This is a gross heuristic.
// Ideally, when we add an element to a corpus we need to know its DFT.
// But if we don't, we'll use the DFT of its base input.
if (II.DataFlowTraceForFocusFunction.empty() && BaseII)
II.DataFlowTraceForFocusFunction = BaseII->DataFlowTraceForFocusFunction;
DistributionNeedsUpdate = true;
PrintCorpus();
// ValidateFeatureSet();
return &II;
}
// Debug-only
void PrintUnit(const Unit &U) {
if (!FeatureDebug) return;
for (uint8_t C : U) {
if (C != 'F' && C != 'U' && C != 'Z')
C = '.';
Printf("%c", C);
}
}
// Debug-only
void PrintFeatureSet(const Vector<uint32_t> &FeatureSet) {
if (!FeatureDebug) return;
Printf("{");
for (uint32_t Feature: FeatureSet)
Printf("%u,", Feature);
Printf("}");
}
// Debug-only
void PrintCorpus() {
if (!FeatureDebug) return;
Printf("======= CORPUS:\n");
int i = 0;
for (auto II : Inputs) {
if (std::find(II->U.begin(), II->U.end(), 'F') != II->U.end()) {
Printf("[%2d] ", i);
Printf("%s sz=%zd ", Sha1ToString(II->Sha1).c_str(), II->U.size());
PrintUnit(II->U);
Printf(" ");
PrintFeatureSet(II->UniqFeatureSet);
Printf("\n");
}
i++;
}
}
void Replace(InputInfo *II, const Unit &U) {
assert(II->U.size() > U.size());
Hashes.erase(Sha1ToString(II->Sha1));
DeleteFile(*II);
ComputeSHA1(U.data(), U.size(), II->Sha1);
Hashes.insert(Sha1ToString(II->Sha1));
II->U = U;
II->Reduced = true;
DistributionNeedsUpdate = true;
}
bool HasUnit(const Unit &U) { return Hashes.count(Hash(U)); }
bool HasUnit(const std::string &H) { return Hashes.count(H); }
InputInfo &ChooseUnitToMutate(Random &Rand) {
InputInfo &II = *Inputs[ChooseUnitIdxToMutate(Rand)];
assert(!II.U.empty());
return II;
}
InputInfo &ChooseUnitToCrossOverWith(Random &Rand, bool UniformDist) {
if (!UniformDist) {
return ChooseUnitToMutate(Rand);
}
InputInfo &II = *Inputs[Rand(Inputs.size())];
assert(!II.U.empty());
return II;
}
// Returns an index of random unit from the corpus to mutate.
size_t ChooseUnitIdxToMutate(Random &Rand) {
UpdateCorpusDistribution(Rand);
size_t Idx = static_cast<size_t>(CorpusDistribution(Rand));
assert(Idx < Inputs.size());
return Idx;
}
void PrintStats() {
for (size_t i = 0; i < Inputs.size(); i++) {
const auto &II = *Inputs[i];
Printf(" [% 3zd %s] sz: % 5zd runs: % 5zd succ: % 5zd focus: %d\n", i,
Sha1ToString(II.Sha1).c_str(), II.U.size(),
II.NumExecutedMutations, II.NumSuccessfullMutations, II.HasFocusFunction);
}
}
void PrintFeatureSet() {
for (size_t i = 0; i < kFeatureSetSize; i++) {
if(size_t Sz = GetFeature(i))
Printf("[%zd: id %zd sz%zd] ", i, SmallestElementPerFeature[i], Sz);
}
Printf("\n\t");
for (size_t i = 0; i < Inputs.size(); i++)
if (size_t N = Inputs[i]->NumFeatures)
Printf(" %zd=>%zd ", i, N);
Printf("\n");
}
void DeleteFile(const InputInfo &II) {
if (!OutputCorpus.empty() && II.MayDeleteFile)
RemoveFile(DirPlusFile(OutputCorpus, Sha1ToString(II.Sha1)));
}
void DeleteInput(size_t Idx) {
InputInfo &II = *Inputs[Idx];
DeleteFile(II);
Unit().swap(II.U);
II.Energy = 0.0;
II.NeedsEnergyUpdate = false;
DistributionNeedsUpdate = true;
if (FeatureDebug)
Printf("EVICTED %zd\n", Idx);
}
void AddRareFeature(uint32_t Idx) {
// Maintain *at least* TopXRarestFeatures many rare features
// and all features with a frequency below ConsideredRare.
// Remove all other features.
while (RareFeatures.size() > Entropic.NumberOfRarestFeatures &&
FreqOfMostAbundantRareFeature > Entropic.FeatureFrequencyThreshold) {
// Find most and second most abbundant feature.
uint32_t MostAbundantRareFeatureIndices[2] = {RareFeatures[0],
RareFeatures[0]};
size_t Delete = 0;
for (size_t i = 0; i < RareFeatures.size(); i++) {
uint32_t Idx2 = RareFeatures[i];
if (GlobalFeatureFreqs[Idx2] >=
GlobalFeatureFreqs[MostAbundantRareFeatureIndices[0]]) {
MostAbundantRareFeatureIndices[1] = MostAbundantRareFeatureIndices[0];
MostAbundantRareFeatureIndices[0] = Idx2;
Delete = i;
}
}
// Remove most abundant rare feature.
RareFeatures[Delete] = RareFeatures.back();
RareFeatures.pop_back();
for (auto II : Inputs) {
if (II->DeleteFeatureFreq(MostAbundantRareFeatureIndices[0]))
II->NeedsEnergyUpdate = true;
}
// Set 2nd most abundant as the new most abundant feature count.
FreqOfMostAbundantRareFeature =
GlobalFeatureFreqs[MostAbundantRareFeatureIndices[1]];
}
// Add rare feature, handle collisions, and update energy.
RareFeatures.push_back(Idx);
GlobalFeatureFreqs[Idx] = 0;
for (auto II : Inputs) {
II->DeleteFeatureFreq(Idx);
// Apply add-one smoothing to this locally undiscovered feature.
// Zero energy seeds will never be fuzzed and remain zero energy.
if (II->Energy > 0.0) {
II->SumIncidence += 1;
II->Energy += logl(II->SumIncidence) / II->SumIncidence;
}
}
DistributionNeedsUpdate = true;
}
bool AddFeature(size_t Idx, uint32_t NewSize, bool Shrink) {
assert(NewSize);
Idx = Idx % kFeatureSetSize;
uint32_t OldSize = GetFeature(Idx);
if (OldSize == 0 || (Shrink && OldSize > NewSize)) {
if (OldSize > 0) {
size_t OldIdx = SmallestElementPerFeature[Idx];
InputInfo &II = *Inputs[OldIdx];
assert(II.NumFeatures > 0);
II.NumFeatures--;
if (II.NumFeatures == 0)
DeleteInput(OldIdx);
} else {
NumAddedFeatures++;
if (Entropic.Enabled)
AddRareFeature((uint32_t)Idx);
}
NumUpdatedFeatures++;
if (FeatureDebug)
Printf("ADD FEATURE %zd sz %d\n", Idx, NewSize);
SmallestElementPerFeature[Idx] = Inputs.size();
InputSizesPerFeature[Idx] = NewSize;
return true;
}
return false;
}
// Increment frequency of feature Idx globally and locally.
void UpdateFeatureFrequency(InputInfo *II, size_t Idx) {
uint32_t Idx32 = Idx % kFeatureSetSize;
// Saturated increment.
if (GlobalFeatureFreqs[Idx32] == 0xFFFF)
return;
uint16_t Freq = GlobalFeatureFreqs[Idx32]++;
// Skip if abundant.
if (Freq > FreqOfMostAbundantRareFeature ||
std::find(RareFeatures.begin(), RareFeatures.end(), Idx32) ==
RareFeatures.end())
return;
// Update global frequencies.
if (Freq == FreqOfMostAbundantRareFeature)
FreqOfMostAbundantRareFeature++;
// Update local frequencies.
if (II)
II->UpdateFeatureFrequency(Idx32);
}
size_t NumFeatures() const { return NumAddedFeatures; }
size_t NumFeatureUpdates() const { return NumUpdatedFeatures; }
private:
static const bool FeatureDebug = false;
size_t GetFeature(size_t Idx) const { return InputSizesPerFeature[Idx]; }
void ValidateFeatureSet() {
if (FeatureDebug)
PrintFeatureSet();
for (size_t Idx = 0; Idx < kFeatureSetSize; Idx++)
if (GetFeature(Idx))
Inputs[SmallestElementPerFeature[Idx]]->Tmp++;
for (auto II: Inputs) {
if (II->Tmp != II->NumFeatures)
Printf("ZZZ %zd %zd\n", II->Tmp, II->NumFeatures);
assert(II->Tmp == II->NumFeatures);
II->Tmp = 0;
}
}
// Updates the probability distribution for the units in the corpus.
// Must be called whenever the corpus or unit weights are changed.
//
// Hypothesis: inputs that maximize information about globally rare features
// are interesting.
void UpdateCorpusDistribution(Random &Rand) {
// Skip update if no seeds or rare features were added/deleted.
// Sparse updates for local change of feature frequencies,
// i.e., randomly do not skip.
if (!DistributionNeedsUpdate &&
(!Entropic.Enabled || Rand(kSparseEnergyUpdates)))
return;
DistributionNeedsUpdate = false;
size_t N = Inputs.size();
assert(N);
Intervals.resize(N + 1);
Weights.resize(N);
std::iota(Intervals.begin(), Intervals.end(), 0);
std::chrono::microseconds AverageUnitExecutionTime(0);
for (auto II : Inputs) {
AverageUnitExecutionTime += II->TimeOfUnit;
}
AverageUnitExecutionTime /= N;
bool VanillaSchedule = true;
if (Entropic.Enabled) {
for (auto II : Inputs) {
if (II->NeedsEnergyUpdate && II->Energy != 0.0) {
II->NeedsEnergyUpdate = false;
II->UpdateEnergy(RareFeatures.size(), Entropic.ScalePerExecTime,
AverageUnitExecutionTime);
}
}
for (size_t i = 0; i < N; i++) {
if (Inputs[i]->NumFeatures == 0) {
// If the seed doesn't represent any features, assign zero energy.
Weights[i] = 0.;
} else if (Inputs[i]->NumExecutedMutations / kMaxMutationFactor >
NumExecutedMutations / Inputs.size()) {
// If the seed was fuzzed a lot more than average, assign zero energy.
Weights[i] = 0.;
} else {
// Otherwise, simply assign the computed energy.
Weights[i] = Inputs[i]->Energy;
}
// If energy for all seeds is zero, fall back to vanilla schedule.
if (Weights[i] > 0.0)
VanillaSchedule = false;
}
}
if (VanillaSchedule) {
for (size_t i = 0; i < N; i++)
Weights[i] = Inputs[i]->NumFeatures
? (i + 1) * (Inputs[i]->HasFocusFunction ? 1000 : 1)
: 0.;
}
if (FeatureDebug) {
for (size_t i = 0; i < N; i++)
Printf("%zd ", Inputs[i]->NumFeatures);
Printf("SCORE\n");
for (size_t i = 0; i < N; i++)
Printf("%f ", Weights[i]);
Printf("Weights\n");
}
CorpusDistribution = std::piecewise_constant_distribution<double>(
Intervals.begin(), Intervals.end(), Weights.begin());
}
std::piecewise_constant_distribution<double> CorpusDistribution;
Vector<double> Intervals;
Vector<double> Weights;
std::unordered_set<std::string> Hashes;
Vector<InputInfo*> Inputs;
size_t NumAddedFeatures = 0;
size_t NumUpdatedFeatures = 0;
uint32_t InputSizesPerFeature[kFeatureSetSize];
uint32_t SmallestElementPerFeature[kFeatureSetSize];
bool DistributionNeedsUpdate = true;
uint16_t FreqOfMostAbundantRareFeature = 0;
uint16_t GlobalFeatureFreqs[kFeatureSetSize] = {};
Vector<uint32_t> RareFeatures;
std::string OutputCorpus;
};
} // namespace fuzzer
#endif // LLVM_FUZZER_CORPUS
Reference in New Issue View Git Blame Copy Permalink