Heap spraying is a security attack that increases the exploitability of memory corruption errors in type-unsafe applications. In a heap-spraying attack, an attacker coerces an application to allocate many objects containing malicious code in the heap, increasing the success rate of an exploit that jumps to a location within the heap. Because heap layout randomization necessitates new forms of attack, spraying has been used in many recent security exploits. Spraying is especially effective in web browsers, where the attacker can easily allocate the malicious objects using JavaScript embedded in a web page. In this paper, we describe NOZZLE, a runtime heap-spraying detector. NOZZLE examines individual objects in the heap, interpreting them as code and performing a static analysis on that code to detect malicious intent. To reduce false positives, we aggregate measurements across all heap objects and define a global heap health metric. We measure the effectiveness of NOZZLE by demonstrating that it successfully detects 12 published and 2,000 synthetically generated heap-spraying exploits. We also show that even with a detection threshold set six times lower than is required to detect published malicious attacks, NOZZLE reports no false positives when run over 150 popular Internet sites. Using sampling and concurrent scanning to reduce overhead, we show that the performance overhead of NOZZLE is less than 7 % on average. While NOZZLE currently targets heap-based spraying attacks, its techniques can be applied to any attack that attempts to fill the address space with malicious code objects (e.g., stack spraying [42]). 1

Most of the recent work on Web security focuses on preventing attacks that directly harm the browser’s host machine and user. In this paper we attempt to quantify the threat of browsers being indirectly misused for attacking third parties. Specifically, we look at how the existing Web infrastructure (e.g., the languages, protocols, and security policies) can be exploited by malicious Web sites to remotely instruct browsers to orchestrate actions including denial of service attacks, worm propagation and reconnaissance scans. We show that, depending mostly on the popularity of a malicious Web site and user browsing patterns, attackers are able to create powerful botnet-like infrastructures that can cause significant damage. We explore the effectiveness of countermeasures including anomaly detection and more fine-grained browser security policies.

Abstract. Network-level emulation has recently been proposed as a method for the accurate detection of previously unknown polymorphic code injection attacks. In this paper, we extend network-level emulation along two lines. First, we present an improved execution behavior heuristic that enables the detection of a certain class of non-self-contained polymorphic shellcodes that are currently missed by existing emulation-based approaches. Second, we present two generic algorithmic optimizations that improve the runtime performance of the detector. We have implemented a prototype of the proposed technique and evaluated it using off-the-shelf non-self-contained polymorphic shellcode engines and benign data. The detector achieves a modest processing throughput, which however is enough for decent runtime performance on actual deployments, while it has not produced any false positives. Finally, we report attack activity statistics from a seven-month deployment of our prototype in a production network, which demonstrate the effectiveness and practicality of our approach. 1

Remotely-launched software exploits are a common way for attackers to intrude into vulnerable computer systems. As detection techniques improve, remote exploitation techniques are also evolving. Recent techniques for evasion of exploit detection include polymorphism (code encryption) and metamorphism (code obfuscation). This paper addresses the problem of detecting in network traffic polymorphic remote exploits that are encrypted, and that self-decrypt before launching the intrusion. Such exploits pose a great challenge to existing malware detection techniques, partly due to the non-obvious starting location of the exploit code in the network payload. We describe a new method for detecting self-decrypting exploit codes. This method scans network traffic for the presence of a decryption routine, which is characteristic of such exploits. The proposed method uses static analysis and emulated instruction execution techniques. This improves the accuracy of determining the starting location and instructions of the decryption routine, even if self-modifying code is used. The method outperforms approaches that have been previously proposed, both in terms of detection capabilities, and in detection accuracy. The proposed method has been implemented and tested on current polymorphic exploits, including ones generated by state-of-the-art polymorphic engines. All exploits have been detected (i.e., a 100 % detection rate), including those for which the decryption routine is dynamically coded, or self-modifying. The false positive rate is close to 0%. Running time is approximately linear in the size of the network payload being analyzed.

Remote code injection attacks against network services remain one of the most effective and widely used exploitation methods for malware propagation. In this paper, we present a study of more than 1.2 million polymorphic code injection attacks targeting production systems, captured using network-level emulation. We focus on the analysis of the structure and operation of the attack code, as well as the overall attack activity in relation to the targeted services. The observed attacks employ a highly diverse set of exploits, often against less widely used vulnerable services, while our results indicate limited use of sophisticated obfuscation schemes and extensive code reuse among different malware families. 1

History indicates that the security community commonly takes a divide-and-conquer approach to battling malware threats: identify the essential and inalienable components of an attack, then develop detection and prevention techniques that directly target one or more of the essential components. This abstraction is evident in much of the literature for buffer overflow attacks including, for instance, stack protection and NOP sled detection. It comes as no surprise then that we approach shellcode detection and prevention in a similar fashion. However, the common belief that components of polymorphic shellcode (e.g., the decoder) cannot reliably be hidden suggests a more implicit and broader assumption that continues to drive contemporary research: namely, that valid and complete representations of shellcode are fundamentally different in structure than benign payloads. While the first tenet of this assumption is philosophically undeniable (i.e., a string of bytes is either shellcode or it is not), truth of the latter claim is less obvious if there exist encoding techniques capable of producing shellcode with features nearly indistinguishable from non-executable content. In this paper, we challenge the assumption that shellcode must conform to superficial and discernible representations. Specifically, we demonstrate a technique for automatically producing English Shellcode, transforming arbitrary shellcode into a representation that is superficially similar to English prose. The shellcode is completely self-contained— i.e., it does not require an external loader and executes as valid IA32 code)—and can typically be generated in under an hour on commodity hardware. Our primary objective in this paper is to promote discussion and stimulate new ideas for thinking ahead about preventive measures for tackling evolutions in code-injection attacks.

A promising method for the detection of previously unknown code injection attacks is the identification of the shellcode that is part of the attack vector using payload execution. Existing systems based on this approach rely on the self-decrypting behavior of polymorphic code and can identify only that particular class of shellcode. Plain, and more importantly, metamorphic shellcode do not carry a decryption routine nor exhibit any self-modifications and thus both evade existing detection systems. In this paper, we present a comprehensive shellcode detection technique that uses a set of runtime heuristics to identify the presence of shellcode in arbitrary data streams. We have identified fundamental machine-level operations that are inescapably performed by different shellcode types, based on which we have designed heuristics that enable the detection of plain and metamorphic shellcode regardless of the use of self-decryption. We have implemented our technique in Gene, a code injection attack detection system based on passive network monitoring. Our experimental evaluation and real-world deployment show that Gene can effectively detect a large and diverse set of shellcode samples that are currently missed by existing detectors, while so far it has not generated any false positives.

Detecting the presence of buffer overflow attacks in network messages has been a major focus. Only knowing whether a message contains an attack, however, is not always enough to mitigate the threat. It may also be critical to know what it does. Unfortunately, shell code is written in low-level assembly language, and can be obfuscated. The current method of analyzing shell code, manual reverse engineering, is time-consuming, requires significant expertise, and would be nearly impossible for a wide-scale polymorphic attack. In this paper, we introduce Spector, a symbolic execution engine that extracts meaningful high-level actions from shell code. Spector’s high-level output helps facilitate attack mitigation and classification of different payloads that have the same behavior. To evaluate Spector, we tested it with over 23,000 unique payloads. It identified eleven different classes of shell code, and processed all the payloads in just over three hours. Spector also successfully classified polymorphic instances of the same shell code. 1.

Abstract. It is always assumed that if the attackers can achieve their goal by exploiting a vulnerability once, they won’t exploit it twice. This assumption shapes our view of what attacks look like, and affects the design of many security systems. In this work, we propose the swarm attack, in which the attacker deliberately exploits the same vulnerability multiple times, each intended to carry out only a small part of the attack goal. We have studied eight systems that detect attacks using networklevel emulation/analysis, and find them surprisingly vulnerable to attacks based on this strategy. Keywords: Decoder detection; network-level emulation; network IDS; evasion; swarm attacks 1

Abstract: This paper discusses a detection mechanism for malicious code through statistical analysis of opcode distributions. A total of 67 malware executables were sampled statically disassembled and their statistical opcode frequency distribution compared with the aggregate statistics of 20 non-malicious samples. We find that malware opcode distributions differ statistically significantly from non-malicious software. Furthermore, rare opcodes seem to be a stronger predictor, explaining 12–63 % of frequency variation.