Can you elaborate on this? To force quicksort into a quadratic running time, you need to ensure that each pivot splits off a bounded number of elements (e.g. no more than three, or no more than twenty million) from the rest of the list. If the pivot is being chosen at random, then it looks to me like the guarantee you'd need to make is "every single element of this list [because any of them might be chosen] is larger, and smaller, than no more than k other elements of the list". But as the size of the list grows, that condition forces it to be mostly composed of the same element repeated over and over again, which is really easy to sort, and in particular is really easy for quicksort to handle.
If you have control of the comparison routine, there's Doug McIlroy's classic "quicksort killer"[0]. If you have information about the state of the random number generator used to pick the pivot, then you can do the same without having to actually interact through the comparison routine. Many libraries use an LCG[1] with 32 to 64 bit state, which is trivial to reconstruct through a small sample of its output not much longer than the internal state.
I don't know about you, but requiring my sort() routine to have access to a cryptographically secure random number generator doesn't seem right; I much prefer any algorithm (e.g. HeapSort, Mergesort or TimSort) that can guarantee n\*log(n) behavior with deterministic (and especially, no secure random generator requirement!) behavior.
[0] http://www.cs.dartmouth.edu/~doug/mdmspe.pdf
[1] http://en.wikipedia.org/wiki/Linear_congruential_generator
It's fully deterministic, as fast as Quicksort in the average case, O(n) in the best case (all equal, but it scales smoothly as the number of unique elements decreases), is optimized for common inputs like ascending/descending (both O(n)) and uses a novel idea of guaranteeing O(n log n) worst case, that guarantees the worst case is no slower than three times the average case, assuming heapsort is twice as slow as Quicksort.
I'm still tweaking the final details and working on the paper, so it's not ready for a full release yet, but a sneak peek here and there is nice :)
This is a benchmark comparing pdqsort against introsort (std::sort), heap sort and timsort: http://i.imgur.com/TSvXnG5.png .
Heapsort implementation vary widely in their cache coherence. The vast majority are extremely simple but result in essentially complete incoherence. I was once able to speed a heapsort about 2x by rearranging the scan order into a cache-oblivious one (Mostly the heap building part; I'm not aware of a way to make the extraction part cache-friendy).
Heapsorts, quicksorts and bubble sorts have the ability to sort just the "top-n" and stop there (unlike mergesort), which is often useful, and a significat speed up (goes from nlog(n) to n+mlog(n) to get top m, or from n^2 to n times m for bubble sort) - I wish library routines actually provided that as part of a standard interface. Perhaps you could be the torchbearer in your publication?
Personally, I think just about every standard library except APL/J/K got sorting wrong. The primary sort operation should be "compute stable ordering permutation", with "sort this array" (basically only available operation in most libraries) being at most a shortcut when you don't care about the permutation.
Also, C++ has always had the "top-n" interface called partial sort: http://en.cppreference.com/w/cpp/algorithm/partial_sort .
And I only mentioned control because that's what McIlroy's example required to use as-is. But as I already stated, it is enough to know in advance the output of the (pseudo) random used to select a pivot to make it go quadratic, which is usually easy -- unless it is cryptographically secure (I've never seen qsort use one. have you?)
So, no, it is completely fair: There is no way to cause mergesort to go quadratic. However, it is quite easy to cause quicksort to go quadratic if you know how it selects it pivots (whether or not you can directly influence it during runtime or not).
If this adversary were forced to realize all the values it fed to the sorter before the sorter did any work, or if it were unable to supply its own code to the sorter, random pivot selection would be a defense.
edit:
I feel like pointing out that a quicksort implementation could defeat this adversary, without hurting its O(n log n) running time, by just comparing the first element of the sublist it was working with to every other element in the sublist -- and throwing away the results -- and then proceeding as normal. This is O(n) comparisons, which violates the vulnerability criterion of making only O(1) comparisons per call, but doesn't affect the big-O running time at all. What it does do, with an eye to this particular adversary, is realize all the values before doing any sorting work. It still doesn't fix the actual vulnerability the paper identifies, which is that you're running adversary-supplied code. I'm growing to feel like your bug report was frivolous.
What you also don't seem to realize is that this attack merely uses a comparison function to find the worst case. Once the worst case is found you can feed this input to any program using libc++'s std::sort, without comparison function, and trigger the worst case.
So no, this is not frivolous at all.
This is true iff pivots are selected deterministically. In which case, why did you post it as a response to "how can an adversary force quadratic behavior when pivots are chosen randomly?"
Specifically, this is a requirement in C++11. Earlier C++ standards only require it to be average-case O(n log n).
The real problem with random quicksort is that randomness is really, really hard, and good RNGs are slow. Also, timsort is adaptive, while quicksort is not.
Then, you would easily get the O(n^2) runtime because your pivot would always be the left most element.
Is Quicksort that much better, or do you just change?
I've never looked into whether a Sedgewick-approved Quicksort can avoid worst case behavior against antiquicksort ( http://www.cs.dartmouth.edu/~doug/aqsort.c , http://www.cs.dartmouth.edu/~doug/mdmspe.pdf ).