Array out of bounds access

[aseyboldt]

I reported this in #8133, but I think it got lost in my confusion about scalar values in mxnet. But I think this deserves a separate issue.

Unlike numpy, mxnet doesn't support rank 0 arrays, but uses an empty tuple to represent unknown shapes. mx.nd still allows the creation of rank 0 arrays however. This leads to confusion during shape inference and allows out of bounds memory reads and writes:

a = mx.sym.var('a')
b = mx.sym.var('b')
c = a + b

a_ = mx.nd.ones(()) # Has size 1, but bind assumes it has shape (5,)
b_ = mx.nd.ones(5)
func = c.bind(mx.cpu(), {'a': a_, 'b': b_})
func.forward()
func.outputs[0].asnumpy()
array([  2.00000000e+00,   2.52435490e-29,   8.84247875e+05,
         1.08446609e-19,  -1.92860745e-26], dtype=float32)

We can write to invalid memory (and crash the process or potentially get arbitrary code execution) by providing a gradient buffer with the wrong shape

a = mx.sym.var('a')
c = mx.sym.MakeLoss(a)

grad = mx.nd.ones(())
a_ = mx.nd.ones(500)
func = c.bind(mx.cpu(), {'a': a_}, args_grad={'a': grad})
func.forward()
func.backward()

[piiswrong]

Thanks for reporting. Need to forbid creation of rank0 array

[szha]

@apache/mxnet-committers: This issue has been inactive for the past 90 days. It has no label and needs triage.

For general "how-to" questions, our user forum (and Chinese version) is a good place to get help.

[vrakesh]

@aseyboldt Are you still seeing this issue, there has been some related PR merges to this issue, If you no longer see this on your side, requesting you to close this.