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

Applications written in unsafe languages like C and C++ are vulnerable to memory errors such as buffer overflows, dangling pointers, and reads of uninitialized data. Such errors can lead to program crashes, security vulnerabilities, and unpredictable behavior. We present DieHard, a runtime system that tolerates these errors while probabilistically maintaining soundness. DieHard uses randomization and replication to achieve probabilistic memory safety by approximating an infinite-sized heap. DieHard’s memory manager randomizes the location of objects in a heap that is at least twice as large as required. This algorithm prevents heap corruption and provides a probabilistic guarantee of avoiding memory errors. For additional safety, DieHard can operate in a replicated mode where multiple replicas of the same application are run simultaneously. By initializing each replica with a different random seed and requiring agreement on output, the replicated version of Die-Hard increases the likelihood of correct execution because errors are unlikely to have the same effect across all replicas. We present analytical and experimental results that show DieHard’s resilience to a wide range of memory errors, including a heap-based buffer overflow in an actual application.

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.

Attacks often exploit memory errors to gain control over the execution of vulnerable programs. These attacks remain a serious problem despite previous research on techniques to prevent them. We present Write Integrity Testing (WIT), a new technique that provides practical protection from these attacks. WIT uses points-to analysis at compile time to compute the control-flow graph and the set of objects that can be written by each instruction in the program. Then it generates code instrumented to prevent instructions from modifying objects that are not in the set computed by the static analysis, and to ensure that indirect control transfers are allowed by the control-flow graph. To improve coverage where the analysis is not precise enough, WIT inserts small guards between the original program objects. We describe an efficient implementation with optimizations to reduce space and time overhead. This implementation can be used in practice because it compiles C and C++ programs without modifications, it has high coverage with no false positives, and it has low overhead. WIT’s average runtime overhead is only 7 % across a set of CPU intensive benchmarks and it is negligible when IO is the bottleneck. 1.

Programs written in C and C++ are susceptible to memory errors, including buffer overflows and dangling pointers. These errors, which can lead to crashes, erroneous execution, and security vulnerabilities, are notoriously costly to repair. Tracking down their location in the source code is difficult, even when the full memory state of the program is available. Once the errors are finally found, fixing them remains challenging: even for critical security-sensitive bugs, the average time between initial reports and the issuance of a patch is nearly 1 month. We present Exterminator, a system that automatically corrects heap-based memory errors without programmer intervention. Exterminator exploits randomization to pinpoint errors with high precision. From this information, Exterminator derives runtime patches that fix these errors both in current and subsequent executions. In addition, Exterminator enables collaborative bug correction by merging patches generated by multiple users. We present analytical and empirical results that demonstrate Exterminator’s effectiveness at detecting and correcting both injected and real faults. 1.

The vulnerabilities that plague computers cause endless grief to users. Slammer compromised millions of hosts in minutes; a hit-list worm would take under a second. Recently proposed techniques respond better than manual approaches, but require expensive instrumentation, which limits deployment. Although spreading “antibodies ” (e.g. signatures) ameliorates this limitation, hosts depending on antibodies are defenseless until inoculation; to the fastest hit-list worms this delay is crucial. Additionally, most recently proposed techniques cannot provide recovery to provide continuous service after an attack. We propose a novel solution called Sweeper that provides both fast and accurate post-attack analysis and efficient recovery with low normal execution overhead. Sweeper innovatively combines several techniques: (1) Sweeper uses lightweight monitoring techniques to detect a wide array of suspicious requests, providing a first level of defense. (2) By cleverly leveraging lightweight checkpointing, Sweeper postpones heavyweight monitoring until absolutely necessary — after an attack is detected. Sweeper rolls back and re-executes multiple times to dynamically apply heavyweight analysis techniques via dynamic binary instrumentation. Since only the execution involved in the attack is analyzed, the analysis is efficient, yet thorough. (3) Based on the analysis results, Sweeper automatically generates lowoverhead antibodies to prevent future attacks of the same vulnerability. (4) Finally, Sweeper again re-executes to perform fast recovery for continuous service. We implement Sweeper in a real system. Our experimental results with three real-world servers and four real security vulnerabilities show that Sweeper can detect an attack and generate antibodies in under 60 milliseconds. Our results also show that Sweeper imposes under 1 % overhead during normal execution, clearly suitable for widespread production deployment (especially since Sweeper also allows partial deployment). Finally, we analytically show that, for a

This paper evaluates pointer tainting, an incarnation of Dynamic Information Flow Tracking (DIFT), which has recently become an important technique in system security. Pointer tainting has been used for two main purposes: detection of privacy-breaching malware (e.g., trojan keyloggers obtaining the characters typed by a user), and detection of memory corruption attacks against non-control data (e.g., a buffer overflow that modifies a user’s privilege level). In both of these cases the attacker does not modify control data such as stored branch targets, so the control flow of the target program does not change. Phrased differently, in terms of instructions executed, the program behaves ‘normally’. As a result, these attacks are exceedingly difficult to detect. Pointer tainting is considered one of the only methods for detecting them in unmodified binaries. Unfortunately, almost all of the incarnations of pointer tainting are flawed. In particular, we demonstrate that the application of pointer tainting to the detection of keyloggers and other privacybreaching malware is problematic. We also discuss whether pointer tainting is able to reliably detect memory corruption attacks against non-control data. We found that pointer tainting generates itself the conditions for false positives. We analyse the problems in detail and investigate various ways to improve the technique. Most have serious drawbacks in that they are either impractical (and incur many false positives still), and/or cripple the technique’s ability to detect attacks. In conclusion, we argue that depending on architecture and operating system, pointer tainting may have some

Buffer overflows have become the most common target for network-based attacks. They are also the primary mechanism used by worms and other forms of automated attacks. Although many techniques have been developed to prevent server compromises due to buffer overflows, these defenses still lead to server crashes. When attacks occur repeatedly, as is common with automated attacks, these protection mechanisms lead to repeated restarts of the victim application, rendering its service unavailable. To overcome this problem, we develop a new approach that can learn the characteristics of a particular attack, and filter out future instances of the same attack or its variants. By doing so, our approach significantly increases the availability of servers subjected to repeated attacks. The approach is fully automatic, does not require source code, and has low runtime overheads. In our experiments, it was effective against most attacks, and did not produce any false positives.

Current taint tracking systems suffer from high overhead and a lack of generality. In this paper, we solve both of these issues with an extensible system that is an order of magnitude more efficient than previous software taint tracking systems and is fully general to dynamic data flow tracking problems. Our system uses a compiler to transform untrusted programs into policy-enforcing programs, and our system can be easily reconfigured to support new analyses and policies without modifying the compiler or runtime system. Our system uses a sound and sophisticated static analysis that can dramatically reduce the amount of data that must be dynamically tracked. For server programs, our system’s average overhead is 0.65% for taint tracking, which is comparable to the best hardware-based solutions. For a set of compute-bound benchmarks, our system produces no runtime overhead because our compiler can prove the absence of vulnerabilities, eliminating the need to dynamically track taint. After modifying these benchmarks to contain format string vulnerabilities, our system’s overhead is less than 13%, which is over 6 × lower than the previous best solutions. We demonstrate the flexibility and power of our system by applying it to file disclosure vulnerabilities, a problem that taint tracking cannot handle. To prevent such vulnerabilities, our system introduces an average runtime overhead of 0.25 % for three open source server programs.

Fine-grained binary instrumentations, such as those for tainttracking, have become very popular in computer security due to their applications in exploit detection, sandboxing, malware analysis, etc. However, practical application of taint-tracking has been limited by high performance overheads. For instance, previous software based techniques for taint-tracking on binary code have typically slowed down programs by a factor of 3 or more. In contrast, source-code based techniques have achieved better performance using high level optimizations. Unfortunately, these optimizations are difficult to perform on binaries since much of the high level program structure required by such static analyses is lost during the compilation process. In this paper, we address this challenge by developing static techniques that can recover some of the higher level structure from x86 binaries. Our new static analysis enables effective optimizations, which are applied in the context of taint tracking. As a result, we achieve a substantial reduction in performance overheads as compared to previous works.