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https://github.com/cea-sec/miasm
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Ajax
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
"""
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This example demonstrates two instrumentation possibility:
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- instrumentation executed at each instruction
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- instrumentation on jitter behavior (here, memory tracking)
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Note: for better performance, one can also extend Codegen to produce
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instrumentation at the C / LLVM level
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"""
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import os
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import time
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from pdb import pm
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from miasm2.analysis.sandbox import Sandbox_Linux_arml
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from miasm2.jitter.emulatedsymbexec import EmulatedSymbExec
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from miasm2.jitter.jitcore_python import JitCore_Python
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# Function called at each instruction
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instr_count = 0
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def instr_hook(jitter):
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global instr_count
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instr_count += 1
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return True
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# Extension of the Python jitter to track memory accesses
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class ESETrackMemory(EmulatedSymbExec):
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"""Emulated symb exec with memory access tracking"""
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def _func_read(self, expr_mem):
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value = super(ESETrackMemory, self)._func_read(expr_mem)
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print "Read %s: %s" % (expr_mem, value)
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return value
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def _func_write(self, symb_exec, dest, data):
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print "Write %s: %s" % (dest, data)
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return super(ESETrackMemory, self)._func_write(symb_exec, dest, data)
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# Parse arguments
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parser = Sandbox_Linux_arml.parser(description="Tracer")
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parser.add_argument("filename", help="ELF Filename")
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options = parser.parse_args()
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# Use our memory tracker
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JitCore_Python.SymbExecClass = ESETrackMemory
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# Create sandbox, forcing Python jitter
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options.jitter = "python"
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sb = Sandbox_Linux_arml(options.filename, options, globals())
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# Force jit one instr per call, and register our callback
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sb.jitter.jit.set_options(jit_maxline=1, max_exec_per_call=1)
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sb.jitter.exec_cb = instr_hook
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# Run
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start_time = time.time()
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sb.run()
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stop_time = time.time()
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assert sb.jitter.run is False
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print "Instr speed: %02.f / sec" % (instr_count / (stop_time - start_time))
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