/Users/hongbochen/Code/ya0guang.github.io/tiddly/tiddlers/$__StoryList_1.tid

 

/Users/hongbochen/Code/ya0guang.github.io/tiddly/tiddlers/$__StoryList_2.tid

 

/Users/hongbochen/Code/ya0guang.github.io/tiddly/tiddlers/$__StoryList.tid

 

`tvm.build`

 July 22, 2021 at 3:25 pm
def build(
    inputs: Union[schedule.Schedule, PrimFunc, IRModule, Mapping[str, IRModule]],
    args: Optional[List[Union[Buffer, tensor.Tensor, Var]]] = None,
    target: Optional[Union[str, Target]] = None,
    target_host: Optional[Union[str, Target]] = None,
    name: Optional[str] = "default_function",
    binds: Optional[Mapping[tensor.Tensor, Buffer]] = None,
):
    """Build a function with arguments as signature. Code will be generated
    for devices coupled with target information.

    Parameters
    ----------
    inputs : Union[tvm.te.schedule.Schedule, tvm.tir.PrimFunc, IRModule, Mapping[str, IRModule]]
        The input to be built

    args : Optional[List[Union[tvm.tir.Buffer, tensor.Tensor, Var]]]
        The argument lists to the function.

    target : Optional[Union[str, Target]]
        The target and option of the compilation.

    target_host : Optional[Union[str, Target]]
        Host compilation target, if target is device.
        When TVM compiles device specific program such as CUDA,
        we also need host(CPU) side code to interact with the driver
        setup the dimensions and parameters correctly.
        target_host is used to specify the host side codegen target.
        By default, llvm is used if it is enabled,
        otherwise a stackvm intepreter is used.

    name : Optional[str]
        The name of result function.

    binds : Optional[Mapping[tensor.Tensor, tvm.tir.Buffer]]
        Dictionary that maps the binding of symbolic buffer to Tensor.
        By default, a new buffer is created for each tensor in the argument.

    Returns
    -------
    ret : tvm.module
        A module that combines both host and device code.

    Examples
    ________
    There are two typical example uses of this function depending on the type
    of the argument `inputs`:
    1. it is an IRModule.

    .. code-block:: python

        n = 2
        A = te.placeholder((n,), name='A')
        B = te.placeholder((n,), name='B')
        C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
        s = tvm.te.create_schedule(C.op)
        m = tvm.lower(s, [A, B, C], name="test_add")
        rt_mod = tvm.build(m, target="llvm")

    2. it is a dict of compilation target to IRModule.

    .. code-block:: python

        n = 2
        A = te.placeholder((n,), name='A')
        B = te.placeholder((n,), name='B')
        C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
        s1 = tvm.te.create_schedule(C.op)
        with tvm.target.cuda() as cuda_tgt:
          s2 = topi.cuda.schedule_injective(cuda_tgt, [C])
          m1 = tvm.lower(s1, [A, B, C], name="test_add1")
          m2 = tvm.lower(s2, [A, B, C], name="test_add2")
          rt_mod = tvm.build({"llvm": m1, "cuda": m2}, target_host="llvm")

    Note
    ----
    See the note on :any:`tvm.target` on target string format.
    """
    if isinstance(inputs, schedule.Schedule):
        if args is None:
            raise ValueError("args must be given for build from schedule")
        input_mod = lower(inputs, args, name=name, binds=binds)
    elif isinstance(inputs, (list, tuple, container.Array)):
        merged_mod = tvm.IRModule({})
        for x in inputs:
            merged_mod.update(lower(x))
        input_mod = merged_mod
    elif isinstance(inputs, (tvm.IRModule, PrimFunc)):
        input_mod = lower(inputs)
    elif not isinstance(inputs, (dict, container.Map)):
        raise ValueError(
            f"Inputs must be Schedule, IRModule or dict of target to IRModule, "
            f"but got {type(inputs)}."
        )

    if not isinstance(inputs, (dict, container.Map)):
        target = Target.current() if target is None else target
        target = target if target else "llvm"
        target_input_mod = {target: input_mod}
    else:
        target_input_mod = inputs

    for tar, mod in target_input_mod.items():
        if not isinstance(tar, (str, Target)):
            raise ValueError("The key of inputs must be str or " "Target when inputs is dict.")
        if not isinstance(mod, tvm.IRModule):
            raise ValueError("inputs must be Schedule, IRModule," "or dict of str to IRModule.")

    target_input_mod, target_host = Target.check_and_update_host_consist(
        target_input_mod, target_host
    )

    if not target_host:
        for tar, mod in target_input_mod.items():
            tar = Target(tar)
            device_type = ndarray.device(tar.kind.name, 0).device_type
            if device_type == ndarray.cpu(0).device_type:
                target_host = tar
                break
    if not target_host:
        target_host = "llvm" if tvm.runtime.enabled("llvm") else "stackvm"

    target_input_mod, target_host = Target.check_and_update_host_consist(
        target_input_mod, target_host
    )

    mod_host_all = tvm.IRModule({})

    device_modules = []
    for tar, input_mod in target_input_mod.items():
        mod_host, mdev = _build_for_device(input_mod, tar, target_host)
        mod_host_all.update(mod_host)
        device_modules.append(mdev)

    # Generate a unified host module.
    rt_mod_host = codegen.build_module(mod_host_all, target_host)

    # Import all modules.
    for mdev in device_modules:
        if mdev:
            rt_mod_host.import_module(mdev)

    if not isinstance(target_host, Target):
        target_host = Target(target_host)
    if (
        target_host.attrs.get("runtime", tvm.runtime.String("c++")) == "c"
        and target_host.attrs.get("system-lib", 0) == 1
    ):
        if target_host.kind.name == "c":
            create_csource_crt_metadata_module = tvm._ffi.get_global_func(
                "runtime.CreateCSourceCrtMetadataModule"
            )
            to_return = create_csource_crt_metadata_module([rt_mod_host], target_host)

        elif target_host.kind.name == "llvm":
            create_llvm_crt_metadata_module = tvm._ffi.get_global_func(
                "runtime.CreateLLVMCrtMetadataModule"
            )
            to_return = create_llvm_crt_metadata_module([rt_mod_host], target_host)
    else:
        to_return = rt_mod_host

    return OperatorModule.from_module(to_return, ir_module_by_target=target_input_mod, name=name)

`wasm_runtime_memory_init`

 July 15, 2021 at 10:05 am
wasm_memory_init_with_pool(void *mem, unsigned int bytes)
{
    mem_allocator_t _allocator = mem_allocator_create(mem, bytes);

    if (_allocator) {
        memory_mode = MEMORY_MODE_POOL;
        pool_allocator = _allocator;
        global_pool_size = bytes;
        return true;
    }
    LOG_ERROR("Init memory with pool (%p, %u) failed.\n", mem, bytes);
    return false;
}
mem_allocator_t mem_allocator_create(void *mem, uint32_t size)
{
    return gc_init_with_pool((char *) mem, size);
}

gc_init_with_pool

The previous function invokes this one:

gc_handle_t
gc_init_with_pool(char *buf, gc_size_t buf_size)
{
    char *buf_end = buf + buf_size;
    char *buf_aligned = (char*)(((uintptr_t) buf + 7) & (uintptr_t)~7);
    char *base_addr = buf_aligned + sizeof(gc_heap_t);
    gc_heap_t *heap = (gc_heap_t*)buf_aligned;
    gc_size_t heap_max_size;

    if (buf_size < APP_HEAP_SIZE_MIN) {
        os_printf("[GC_ERROR]heap init buf size (%u) < %u\n",
                  buf_size, APP_HEAP_SIZE_MIN);
        return NULL;
    }

    base_addr = (char*) (((uintptr_t) base_addr + 7) & (uintptr_t)~7) + GC_HEAD_PADDING;
    heap_max_size = (uint32)(buf_end - base_addr) & (uint32)~7;

    return gc_init_internal(heap, base_addr, heap_max_size);
}

and invokes the internal implementation:

static gc_handle_t
gc_init_internal(gc_heap_t *heap, char *base_addr, gc_size_t heap_max_size)
{
    hmu_tree_node_t *root = NULL, *q = NULL;
    int ret;

    memset(heap, 0, sizeof *heap);
    memset(base_addr, 0, heap_max_size);

    ret = os_mutex_init(&heap->lock);
    if (ret != BHT_OK) {
        os_printf("[GC_ERROR]failed to init lock\n");
        return NULL;
    }

    /* init all data structures*/
    heap->current_size = heap_max_size;
    heap->base_addr = (gc_uint8*)base_addr;
    heap->heap_id = (gc_handle_t)heap;

    heap->total_free_size = heap->current_size;
    heap->highmark_size = 0;

    root = &heap->kfc_tree_root;
    memset(root, 0, sizeof *root);
    root->size = sizeof *root;
    hmu_set_ut(&root->hmu_header, HMU_FC);
    hmu_set_size(&root->hmu_header, sizeof *root);

    q = (hmu_tree_node_t *) heap->base_addr;
    memset(q, 0, sizeof *q);
    hmu_set_ut(&q->hmu_header, HMU_FC);
    hmu_set_size(&q->hmu_header, heap->current_size);

    hmu_mark_pinuse(&q->hmu_header);
    root->right = q;
    q->parent = root;
    q->size = heap->current_size;

    bh_assert(root->size <= HMU_FC_NORMAL_MAX_SIZE);

#if WASM_ENABLE_MEMORY_TRACING != 0
    os_printf("Heap created, total size: %u\n", buf_size);
    os_printf("   heap struct size: %u\n", sizeof(gc_heap_t));
    os_printf("   actual heap size: %u\n", heap_max_size);
    os_printf("   padding bytes: %u\n",
              buf_size - sizeof(gc_heap_t) - heap_max_size);
#endif
    return heap;
}

A Design and Verification Methodology for Secure Isolated Regions

 May 3, 2021 at 11:21 am

PLDI'16

PDF

Hardware support for isolated execution (such as Intel SGX) enables development of applications that keep their code and data confidential even while running in a hostile or compromised host. However, automatically verifying that such applications satisfy confidentiality remains challenging. We present a methodology for designing such applications in a way that enables certifying their confidentiality. Our methodology consists of forcing the application to communicate with the external world through a narrow interface, compiling it with runtime checks that aid verification, and linking it with a small runtime that implements the narrow interface. The runtime includes services such as secure communication channels and memory management. We formalize this restriction on the application as Information Release Confinement (IRC), and we show that it allows us to decompose the task of proving confidentiality into (a) one-time, human-assisted functional verification of the runtime to ensure that it does not leak secrets, (b) automatic verification of the application's machine code to ensure that it satisfies IRC and does not directly read or corrupt the runtime's internal state. We present /CONFIDENTIAL: a verifier for IRC that is modular, automatic, and keeps our compiler out of the trusted computing base. Our evaluation suggests that the methodology scales to real-world applications.

Purpose

  • Protect IRC: to prevent info leakage
  • WCFI-RW: weak form of control-flow integrity, and along with restrictions on reads and writes
  • Formally verify a set of checks to enforce WCFI-RW in SIRs
  • Not to depend on a specific compiler, which is not in the TCB

Threat model

  • Application U (in SIR) is not malicious but may contain bugs
  • U may also use third-party libraries
  • The system other than SIR is hostile/compromised

Design

  • U and libraries(L) are verified separately
  • U can only use L to send and recv data outside of SIR

Method

  1. Establish a formal model of U's syntax
  2. Define what an adversary can do under this threat model, formally
  3. Define the target (confidentiality)
  4. Deduce the properties required for WCFI-RW, both for applications and libraries
  5. Derive and instrument checks in code
  6. Verify the properties still hold.

Notice that

  • These checks derived from the properties ensures WCFI-RW
  • The verification may scale

Insights

  • The library may also compromise the app
  • No check of jump into the middle of instrumentation(s)
  • Assumes the page permissions can be changed
  • Only needs SP is in U???

A Formal Foundation for Secure Remote Execution of Enclaves

 June 8, 2022 at 6:57 pm

Paper

Abstract

Recent proposals for trusted hardware platforms, such as Intel SGX and the MIT Sanctum processor, offer compelling security features but lack formal guarantees. We introduce a verification methodology based on a trusted abstract platform (TAP), a formalization of idealized enclave platforms along with a parameterized adversary. We also formalize the notion of secure remote execution and present machine-checked proofs showing that the TAP satisfies the three key security properties that entail secure remote execution: integrity, confidentiality and secure measurement. We then present machine-checked proofs showing that SGX and Sanctum are refinements of the TAP under certain parameterizations of the adversary, demonstrating that these systems implement secure enclaves for the stated adversary models.

Methodology

The enclave is formally modeled, including the program, state (e.g., memory, cache, registers), I/O, execution, and adversaries. The adversaries with privilege, cache access, and memory access are modeled differently.

A trusted abstract platform (TAP) is built for modeling secure remote execution (SRE), which is defined formally and can be meet by satisfying three properties at the same time: Secure Measurement, Integrity, Confidentiality.

Then, to prove that Sanctum and SGX satisfies SRE, the authors prove these architectures refine TAP in Boogie, and therefore meets SRE.

Strength

  • Side-channel and Side-channel attackers are modeled
  • TAP is a general model for various TEEs
  • Formal proof

Weakness

  • The refinement is not accurate enough, i.e., remote attestation seems missing
  • Source code?

A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification

 November 25, 2023 at 11:42 am

arxiv

Abstract

In this paper we present a novel solution that combines the capabilities of Large Language Models (LLMs) with Formal Verification strategies to verify and automatically repair software vulnerabilities. Initially, we employ Bounded Model Checking (BMC) to locate the software vulnerability and derive a counterexample. The counterexample provides evidence that the system behaves incorrectly or contains a vulnerability. The counterexample that has been detected, along with the source code, are provided to the LLM engine. Our approach involves establishing a specialized prompt language for conducting code debugging and generation to understand the vulnerability's root cause and repair the code. Finally, we use BMC to verify the corrected version of the code generated by the LLM. As a proof of concept, we create ESBMC-AI based on the Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained Transformer model, specifically gpt-3.5-turbo, to detect and fix errors in C programs. Our experimentation involved generating a dataset comprising 1000 C code samples, each consisting of 20 to 50 lines of code. Notably, our proposed method achieved an impressive success rate of up to 80% in repairing vulnerable code encompassing buffer overflow and pointer dereference failures. We assert that this automated approach can effectively incorporate into the software development lifecycle's continuous integration and deployment (CI/CD) process.

Summarize

This paper interface LLM with BMC (Bounded model checking) to conform bugs in C code. BMC (based on SMT solver) improves the arithmetic performance, which is not good in LLM. However, I tested the cases which said to fail in LLM on GPT 4 (Ver Aug. 3) whereas it could find the vulnerability, and this is inconsistent with the paper.

The evaluation uses 1000 cases generated by LLM. It's still unclear if such methodology is applicable to very large code base/functions or on real world code base.

A Study of Modern Linux API Usage and Compatibility: What to Support When You’re Supporting

 April 4, 2021 at 10:51 am

EuroSys'16 Best Paper

link

This paper supports the author's following work, graphene(-SGX).

Targeted Interfaces (APIs)

  • System Calls
  • Proc Filesystem

Method

  • Measured the download and dependency data of packages in Ubuntu 15.04.
  • Statically analyzed what syscalls will be invoked in a package and what path will be accessed by a binary (hardcoded string)

Measurement

  • API Importance: A Metric for Individual APIs
  • Weighted Completeness: A System-Wide Metric (combined with #downloads)

Questions Answered

  • Which syscalls are more important than others?
  • Which syscalls can be deprecated? (some are rarely used)
  • Which syscalls should be supported for compatibility?

A Survey on Mix Networks and Their Secure Applications

 November 30, 2021 at 12:28 pm

  • Multiple stages

  • Layers(onion) in encryption