94,909 research outputs found
CUP: Comprehensive User-Space Protection for C/C++
Memory corruption vulnerabilities in C/C++ applications enable attackers to
execute code, change data, and leak information. Current memory sanitizers do
no provide comprehensive coverage of a program's data. In particular, existing
tools focus primarily on heap allocations with limited support for stack
allocations and globals. Additionally, existing tools focus on the main
executable with limited support for system libraries. Further, they suffer from
both false positives and false negatives.
We present Comprehensive User-Space Protection for C/C++, CUP, an LLVM
sanitizer that provides complete spatial and probabilistic temporal memory
safety for C/C++ program on 64-bit architectures (with a prototype
implementation for x86_64). CUP uses a hybrid metadata scheme that supports all
program data including globals, heap, or stack and maintains the ABI. Compared
to existing approaches with the NIST Juliet test suite, CUP reduces false
negatives by 10x (0.1%) compared to the state of the art LLVM sanitizers, and
produces no false positives. CUP instruments all user-space code, including
libc and other system libraries, removing them from the trusted code base
Heap Abstractions for Static Analysis
Heap data is potentially unbounded and seemingly arbitrary. As a consequence,
unlike stack and static memory, heap memory cannot be abstracted directly in
terms of a fixed set of source variable names appearing in the program being
analysed. This makes it an interesting topic of study and there is an abundance
of literature employing heap abstractions. Although most studies have addressed
similar concerns, their formulations and formalisms often seem dissimilar and
some times even unrelated. Thus, the insights gained in one description of heap
abstraction may not directly carry over to some other description. This survey
is a result of our quest for a unifying theme in the existing descriptions of
heap abstractions. In particular, our interest lies in the abstractions and not
in the algorithms that construct them.
In our search of a unified theme, we view a heap abstraction as consisting of
two features: a heap model to represent the heap memory and a summarization
technique for bounding the heap representation. We classify the models as
storeless, store based, and hybrid. We describe various summarization
techniques based on k-limiting, allocation sites, patterns, variables, other
generic instrumentation predicates, and higher-order logics. This approach
allows us to compare the insights of a large number of seemingly dissimilar
heap abstractions and also paves way for creating new abstractions by
mix-and-match of models and summarization techniques.Comment: 49 pages, 20 figure
Do-It-Yourself Single Camera 3D Pointer Input Device
We present a new algorithm for single camera 3D reconstruction, or 3D input
for human-computer interfaces, based on precise tracking of an elongated
object, such as a pen, having a pattern of colored bands. To configure the
system, the user provides no more than one labelled image of a handmade
pointer, measurements of its colored bands, and the camera's pinhole projection
matrix. Other systems are of much higher cost and complexity, requiring
combinations of multiple cameras, stereocameras, and pointers with sensors and
lights. Instead of relying on information from multiple devices, we examine our
single view more closely, integrating geometric and appearance constraints to
robustly track the pointer in the presence of occlusion and distractor objects.
By probing objects of known geometry with the pointer, we demonstrate
acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio
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