Software vulnerabilities have had a devastating effect on the Internet. Worms such as CodeRed and Slammer can compromise hundreds of thousands of hosts within hours or even minutes, and cause millions of dollars of damage [25, 42]. To successfully combat these fast automatic Internet attacks, we need fast automatic attack detection and filtering mechanisms. In this paper we propose dynamic taint analysis for automatic detection of overwrite attacks, which include most types of exploits. This approach does not need source code or special compilation for the monitored program, and hence works on commodity software. To demonstrate this idea, we have implemented TaintCheck, a mechanism that can perform dynamic taint analysis by performing binary rewriting at run time. We show that TaintCheck reliably detects most types of exploits. We found that TaintCheck produced no false positives for any of the many different programs that we tested. Further, we describe how Taint-Check could improve automatic signature generation in several ways. 1.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations. In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis: a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing: an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead

Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a derandomization attack that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original, albeit somewhat slower: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack. We also explore various ways of strengthening addressspace randomization and point out weaknesses in each. Surprisingly, increasing the frequency of re-randomizations adds at most 1 bit of security. Furthermore, compile-time randomization appears to be more effective than runtime randomization. We conclude that, on 32-bit architectures, the only benefit of PaX-like address-space randomization is a small slowdown in worm propagation speed. The cost of randomization is extra complexity in system support.

Many remote attacks against computer systems inject binary code into the execution path of a running program, gaining control of the program's behavior. If each defended system or program could use a machine instruction set that was both unique and private, such binary code injection attacks would become extremely difficult if not impossible. A binary-to-binary translator provides an economic and flexible implementation path for realizing that idea. As a proof of concept, we describe a randomized instruction set emulator (RISE) based on the open-source Valgrind x86-to-x86 binary translator. Although currently very slow and memory-intensive, our prototype RISE can indeed disrupt binary code injection attacks against a program without requiring its recompilation, linking, or access to source code. We describe the RISE implementation, give evidence demonstrating that RISE defeats common attacks, consider consequences of the dense x86 instruction set on the method's effects, and discuss limitations of the RISE prototype as well as design tradeoffs and extensions of the underlying idea.

We propose a new approach for reacting to a wide variety of software failures, ranging from remotely exploitable vulnerabilities to more mundane bugs that cause abnormal program termination (e.g., illegal memory dereference). Our emphasis is in creating "self-healing" software that can protect itself against a recurring fault until a more comprehensive fix is applied.

Web applications are popular targets of security attacks. One common type of such attacks is SQL injection, where an attacker exploits faulty application code to execute maliciously crafted database queries. Both static and dynamic approaches have been proposed to detect or prevent SQL injections; while dynamic approaches provide protection for deployed software, static approaches can detect potential vulnerabilities before software deployment. Previous static approaches are mostly based on tainted information flow tracking and have at least some of the following limitations: (1) they do not model the precise semantics of input sanitization routines; (2) they require manually written specifications, either for each query or for bug patterns; or (3) they are not fully automated and may require user intervention at various points in the analysis. In this paper, we address these limitations by proposing a precise, sound, and fully automated analysis technique for SQL injection. Our technique avoids the need for specifications by considering as attacks those queries for which user input changes the intended syntactic structure of the generated query. It checks conformance to this policy by conservatively characterizing the values a string variable may assume with a context free grammar, tracking the nonterminals that represent user-modifiable data, and modeling string operations precisely as language transducers. We have implemented the proposed technique for PHP, the most widely-used web scripting language. Our tool successfully discovered previously unknown and sometimes subtle vulnerabilities in real-world programs, has a low false positive rate, and scales to large programs (with approx. 100K loc).

Large-scale attacks, such as those launched by worms and zombie farms, pose a serious threat to our network-centric society. Existing approaches such as software patches are simply unable to cope with the volume and speed with which new vulnerabilities are being discovered. In this paper, we develop a new approach that can provide effective protection against a vast majority of these attacks that exploit memory errors in C/C++ programs. Our approach, called COVERS, uses a forensic analysis of a victim server's memory to correlate attacks to inputs received over the network, and automatically develop a signature that characterizes inputs that carry attacks. The signatures tend to capture characteristics of the underlying vulnerability (e.g., a message field being too long) rather than the characteristics of an attack, which makes them effective against variants of attacks. Our approach introduces low overheads (under 10%), does not require access to source code of the protected server, and has successfully generated signatures for the attacks studied in our experiments, without producing false positives. Since the signatures are generated in tens of milliseconds, they can potentially be distributed quickly over the Internet to filter out (and thus stop) fastspreading worms. Another interesting aspect of our approach is that it can defeat guessing attacks reported against address-space randomization and instruction set randomization techniques. Finally, it increases the capacity of servers to withstand repeated attacks by a factor of 10 or more.

We present a practical protection mechanism against SQL injection attacks. Such attacks target databases that are accessible through a web frontend, and take advantage of flaws in the input validation logic of Web components such as CGI scripts. We apply the concept of instruction-set randomization to SQL, creating instances of the language that are unpredictable to the attacker.

Despite the wide publicity received by buffer overflow attacks, the vast majority of today’s security vulnerabilities continue to be caused by memory errors, with a significant shift away from stack-smashing exploits to newer attacks such as heap overflows, integer overflows, and format-string attacks. While comprehensive solutions have been developed to handle memory errors, these solutions suffer from one or more of the following problems: high overheads (often exceeding 100%), incompatibility with legacy C code, and changes to the memory model to use garbage collection. Address space randomization (ASR) is a technique that avoids these drawbacks, but existing techniques for ASR do not offer a level of protection comparable to the above techniques. In particular, attacks that exploit relative distances between memory objects aren’t tackled by existing techniques. Moreover, these techniques are susceptible to information leakage and brute-force attacks. To overcome these limitations, we develop a new approach in this paper that supports comprehensive randomization, whereby the absolute locations of all (code and data) objects, as well as their relative distances are randomized. We argue that this approach provides probabilistic protection against all memory error exploits, whether they be known or novel. Our approach is implemented as a fully automatic source-to-source transformation which is compatible with legacy C code. The address-space randomizations take place at load-time or runtime, so the same copy of the binaries can be distributed to everyone — this ensures compatibility with today’s software distribution model. Experimental results demonstrate an average runtime overhead of about 11%.

for advertised honeypots with automatic signature generation