Asia Slowinska

Minemu: the world's fastest taint tracker

Dynamic taint analysis is a powerful technique to detect memory corruption attacks. However, with typical overheads of an order of magnitude, current implementations are not suitable for most production systems. The research question we address in this paper is whether the slow-down is a fundamental speed barrier, or an artifact of bolting information flow tracking on emulators really not designed for it. In other words, we designed a new type of emulator from scratch with the goal of removing superfluous instructions to propagate taint. The results are very promising. The emulator, known as Minemu, incurs a slowdown of 1.5x-3x for real and complex applications and 2.4x for SPEC INT2006, while tracking taint at byte level granularity. Minemus performance is significantly better than that of existing systems, despite the fact that we have not applied some of their optimizations yet. We believe that the new design may be suitable for certain classes of applications in production systems.

Practical Context-Sensitive CFI

Current Control-Flow Integrity (CFI) implementations track control edges individually, insensitive to the context of preceding edges. Recent work demonstrates that this leaves sufficient leeway for powerful ROP attacks. Context-sensitive CFI, which can provide enhanced security, is widely considered impractical for real-world adoption. Our work shows that Context-sensitive CFI (CCFI) for both the backward and forward edge can be implemented efficiently on commodity hardware. We present PathArmor, a binary-level CCFI implementation which tracks paths to sensitive program states, and defines the set of valid control edges within the state context to yield higher precision than existing CFI implementations. Even with simple context-sensitive policies, PathArmor yields significantly stronger CFI invariants than context-insensitive CFI, with similar performance.

SafeCard: a gigabit IPS on the network card

Current intrusion detection systems have a narrow scope. They target flow aggregates, reconstructed TCP streams, individual packets or application-level data fields, but no existing solution is capable of handling all of the above. Moreover, most systems that perform payload inspection on entire TCP streams are unable to handle gigabit link rates. We argue that network-based intrusion detection systems should consider all levels of abstraction in communication (packets, streams, layer-7 data units, and aggregates) if they are to handle gigabit link rates in the face of complex application-level attacks such as those that use evasion techniques or polymorphism. For this purpose, we developed a framework for network-based intrusion prevention at the network edge that is able to cope with all levels of abstraction and can be easily extended with new techniques. We validate our approach by making available a practical system, SafeCard, capable of reconstructing and scanning TCP streams at gigabit rates while preventing polymorphic buffer-overflow attacks, using (up to) layer-7 checks. Such performance makes it applicable in-line as an intrusion prevention system. SafeCard merges multiple solutions, some new and some known. We made specific contributions in the implementation of deep-packet inspection at high speeds and in detecting and filtering polymorphic buffer overflows.

The Age of Data: Pinpointing Guilty Bytes in Polymorphic Buffer Overflows on Heap or Stack

Heap and stack buffer overflows are still among the most common attack vectors in intrusion attempts. In this paper, we ask a simple question that is surprisingly difficult to answer: which bytes contributed to the overflow? By careful observation of all scenarios that may occur in overflows, we identified the information that needs to be tracked to pinpoint the offending bytes. There are many reasons why this is a hard problem. For instance, by the time an overflow is detected some of the bytes may already have been overwritten, creating gaps. Additionally, it is hard to tell the offending bytes apart from unrelated network data. In our solution, we tag data from the network with an age stamp whenever it is written to a buffer. Doing so allows us to distinguish between different bytes and ignore gaps, and provide precise analysis of the offending bytes. By tracing these bytes to protocol fields, we obtain accurate signatures that cater to polymorphic attacks.

Who allocated my memory? Detecting custom memory allocators in C binaries

Many reversing techniques for data structures rely on the knowledge of memory allocation routines. Typically, they interpose on the systems malloc and free functions, and track each chunk of memory thus allocated as a data structure. However, many performance-critical applications implement their own custom memory allocators. Examples include webservers, database management systems, and compilers like gcc and clang. As a result, current binary analysis techniques for tracking data structures fail on such binaries. We present MemBrush, a new tool to detect memory allocation and deallocation functions in stripped binaries with high accuracy. We evaluated the technique on a large number of real world applications that use custom memory allocators. As we show, we can furnish existing reversing tools with detailed information about the memory management API, and as a result perform an analysis of the actual application specific data structures designed by the programmer. Our system uses dynamic analysis and detects memory allocation and deallocation routines by searching for functions that comply with a set of generic characteristics of allocators and deallocators.

MemPick: High-level data structure detection in C/C++ binaries

Many existing techniques for reversing data structures in C/C++ binaries are limited to low-level programming constructs, such as individual variables or structs. Unfortunately, without detailed information about a programs pointer structures, forensics and reverse engineering are exceedingly hard. To fill this gap, we propose MemPick, a tool that detects and classifies high-level data structures used in stripped binaries. By analyzing how links between memory objects evolve throughout the program execution, it distinguishes between many commonly used data structures, such as singly- or doubly-linked lists, many types of trees (e.g., AVL, red-black trees, B-trees), and graphs. We evaluate the technique on 10 real world applications and 16 popular libraries. The results show that MemPick can identify the data structures with high accuracy.

DDE: dynamic data structure excavation

Dynamic Datastructure Excavation (DDE) is a new approach to extract datastructures from C binaries without any need for debugging symbols. Unlike most existing tools, DDE uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory. Its results are much more accurate than those of previous methods.

Compiler-Agnostic Function Detection in Binaries

We propose Nucleus, a novel function detection algorithm for binaries. In contrast to prior work, Nucleus is compiler-agnostic, and does not require any learning phase or signature information. Instead of scanning for signatures, Nucleus detects functions at the Control Flow Graph-level, making it inherently suitable for difficult cases such as non-contiguous or multi-entry functions. We evaluate Nucleus on a diverse set of 476 C and C ++ binaries, compiled with gcc, clang and Visual Studio for x86 and x64, at optimization levels O0–O3. We achieve consistently good performance, with a mean F-score of 0.95.

System-Level support for intrusion recovery

Recovering from attacks is hard and gets harder as the time between the initial infection and its detection increases. Which files did the attackers modify? Did any of user data depend on malicious inputs? Can I still trust my own documents or binaries? When malcode has been active for some time and its actions are mixed with those of benign applications, these questions are impossible to answer on current systems. In this paper, we describe DiskDuster, an attack analysis and recovery system capable of recovering from complicated attacks in a semi-automated manner. DiskDuster traces malcode at byte-level granularity both in memory and on disk in a modified version of QEMU. Using taint analysis, DiskDuster also tracks all bytes written by the malcode, to provide a detailed view on what (bytes in) files derive from malicious data. Next, it uses this information to remove malicious actions at recovery time.

Scalable data structure detection and classification for C/C++ binaries

Many existing techniques for reversing data structures in C/C ++ binaries are limited to low-level programming constructs, such as individual variables or structs. Unfortunately, without detailed information about a programs pointer structures, forensics and reverse engineering are exceedingly hard. To fill this gap, we propose MemPick, a tool that detects and classifies high-level data structures used in stripped binaries. By analyzing how links between memory objects evolve throughout the program execution, it distinguishes between many commonly used data structures, such as singly- or doubly-linked lists, many types of trees (e.g., AVL, red-black trees, B-trees), and graphs. We evaluate the technique on 10 real world applications, 4 file system implementations and 16 popular libraries. The results show that MemPick can identify the data structures with high accuracy.