In common anecdotal experience with disassembling code, it is very common for data areas interspersed with code (like string literals) to disaassemble to instructions, momentarily causing the human to be puzzled: what is this repetition of five "or" instructions doing here referencing registers that would never be arguments?

The reason is that the opcode encoding is very dense, and has no redundancy against detecting bad encodings, and usually no relationship to neighboring words.

By that I mean that some four byte chunk (say) treated as an opcode word is treated that way regardless of what came before or what comes after. If it looks like an opcode with a four-byte immediate operand, then the disassembly will pull in that operand (which can be any bit combination) and skip another four bytes. Nothing in the operand will indicate "this is a bad instruction overall".

But look, it almost looks as if the Static Huffman (a simpler encoding of compression with fewer decoding errors) almost bears out a certain aspect of the friend's intuition, in the following way:

* only 4.4% of the random data disassembles.

* only 4.0% of the random data decodes as Static Huffman.

BUT:

* 1.2% of the data decompresses and disassembles.

Relative to the 4.0% decompression, 1.2% is 30%.

In other words, 30% of successfully decompressed material also disassembles.

That's something that could benefit from an explanation.

Why is that, evidently, the conditional probability of a good disassemble, given a successful Static Huffman expansion, much higher than the probability of a disassemble from random data?

Another interesting thing… random data has a high likely hood of disassembling into random instructions, but there’s a low probability that such instructions (particularly sequences of such instructions) are valid semantically.

For example, there’s a very high chance a single random instruction would page fault.

If you want to generate random instructions and have them execute, you have to write a tiny debugger, intercept the page faults, fix up the program’s virtual memory map, then re-run the instruction to make it work.

This means that even though high entropy data has a good chance of producing valid instructions, it doesn’t have a high chance of producing valid instruction sequences.

Code that actually does something will have much much lower entropy.

That is interesting…even though random data is syntactically valid as instructions, it’s almost certainly invalid semantically.

I believe this is the third or fourth posting of this article in the last week.

Why the AI disclosure? Is it just for the author to make sure the readers know they are AI-skeptic and use the opportunity to link to another article, or would there be something wrong with the proof had AI been used to help write the code?

(By help I mean just help, not write an entire sloppy article.)