moyix

import json
import argparse
import html
import os
from datetime import datetime

def generate_html(json_file, html_file="chat.html", metadata=None, date=None):
    with open(json_file, 'r') as file:
        data = json.load(file)

moyix

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>

// Build:
// gcc -gdwarf-4 -fcf-protection=none -no-pie -fno-stack-protector basicbof.c -o basicbof

// To give us a pop rdi gadget
void dosomething() {
    int x = 0xc35f;

moyix

import sys
import torch
from transformers import StoppingCriteria, StoppingCriteriaList
from transformers import AutoTokenizer, AutoModelForCausalLM

class StreamPrinter(StoppingCriteria):
    def __init__(self):
        StoppingCriteria.__init__(self)
        self.pos = 0
    def __call__(self, input_ids, scores):

moyix

MONGODB_URL=mongodb://localhost:27017
HF_ACCESS_TOKEN=<REDACTED>

# 'name', 'userMessageToken', 'assistantMessageToken' are required
MODELS=`[
  {
    "endpoints": [{"url": "http://localhost:8081"}],
    "name": "WizardLM/WizardLM-70B-V1.0",
    "description": "WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions",
    "websiteUrl": "https://huggingface.co/WizardLM/WizardLM-70B-V1.0",

moyix

#!/usr/bin/env python3
from flask import Flask, request, redirect
import os
import subprocess
import sys
import yaml
import MySQLdb

app = Flask(__name__)

moyix

{
    "fp16": {
        "enabled": true,
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "initial_scale_power": 16,
        "hysteresis": 2,
        "min_loss_scale": 1
    },
    "optimizer": {

moyix

Total instructions:     48093488942
Total SSE instructions:   100105422
Total XMM instructions:   877832653
Totals by sanitizer:
   ASAN: SSE:   39197160, XMM:  308790743
   MSAN: SSE:   29922931, XMM:  342062480
  UBSAN: SSE:   30985331, XMM:  226979430
All projects per sanitizer, sorted by percent of SSE instructions:
ASAN:                               SSE Instr /      Total =   Pct ↓   Wilson
    simd                         :    1122000 /   63479115 =  1.77 % ( 1.76 %)

moyix

The ffast and the Furious

This is a small and admittedly contrived demo showing how some weird but safe code could become vulnerable if run in an environment where some shared library has changed the FPU's FTZ/DAZ bits to force denormals to zero.

To run it:

# Create an empty file
$ touch gofast.c      

moyix

#===============================================================
#
#
#===============================================================
import shutil
from setuptools import find_packages, setup
from os.path import exists,join,relpath
import os
import stat

moyix

#!/usr/bin/env python

import sys, os
import platform
import ctypes as ct
import mmap
from enum import Enum
import importlib
import functools
import errno