Profile-guided automated software diversity

Homescu, Andrei; Neisius, Steven; Larsen, Per; Brunthaler, Stefan; Franz, Michael

doi:10.1109/cgo.2013.6494997

Cited by 93 publications

(71 citation statements)

References 23 publications

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“…1%) [25]. By diversifying the code, we can be confident that a remote attacker will not be able to accurately predict the locations of gadgets he or she needs to use.…”

Section: Diversifying Gadget Locationsmentioning

confidence: 99%

Booby trapping software

Crane

Larsen

Brunthaler

et al. 2013

Proceedings of the 2013 New Security Paradigms Workshop

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Cyber warfare is asymmetric in the current paradigm, with attackers having the high ground over defenders. This asymmetry stems from the situation that attackers have the initiative, while defenders concentrate on passive fortifications. Defenders are constantly patching the newest hole in their defenses and creating taller and thicker walls, without placing guards on those walls to watch for the enemy and react to attacks. Current passive cyber security defenses such as intrusion detection, anti-virus, and hardened software are not sufficient to repel attackers. In fact, in conventional warfare this passivity would be entirely nonsensical, given the available active strategies, such as counterattacks and deception.Based on this observation, we have identified the technique of booby trapping software. This extends the arsenal of weaponry available to defenders with an active technique for directly reacting to attacks. Ultimately, we believe this approach will restore some of the much sought after equilibrium between attackers and defenders in the digital domain.

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“…1%) [25]. By diversifying the code, we can be confident that a remote attacker will not be able to accurately predict the locations of gadgets he or she needs to use.…”

Section: Diversifying Gadget Locationsmentioning

confidence: 99%

Booby trapping software

Crane

Larsen

Brunthaler

et al. 2013

Proceedings of the 2013 New Security Paradigms Workshop

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“…Similarly to ASAP, the Multicompiler project [14] improves a program's security by focusing efforts on the cold parts of the program.…”

Section: Background and Related Workmentioning

confidence: 99%

High System-Code Security with Low Overhead

Wagner

Kuznetsov

Candea

et al. 2015

2015 IEEE Symposium on Security and Privacy

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Abstract-Security vulnerabilities plague modern systems because writing secure systems code is hard. Promising approaches can retrofit security automatically via runtime checks that implement the desired security policy; these checks guard critical operations, like memory accesses. Alas, the induced slowdown usually exceeds by a wide margin what system users are willing to tolerate in production, so these tools are hardly ever used. As a result, the insecurity of real-world systems persists.We present an approach in which developers/operators can specify what level of overhead they find acceptable for a given workload (e.g., 5%); our proposed tool ASAP then automatically instruments the program to maximize its security while staying within the specified "overhead budget." Two insights make this approach effective: most overhead in existing tools is due to only a few "hot" checks, whereas the checks most useful to security are typically "cold" and cheap.We evaluate ASAP on programs from the Phoronix and SPEC benchmark suites. It can precisely select the best points in the security-performance spectrum. Moreover, we analyzed existing bugs and security vulnerabilities in RIPE, OpenSSL, and the Python interpreter, and found that the protection level offered by the ASAP approach is sufficient to protect against all of them.

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“…Profile information is also used to direct many other optimization tasks. For instance, profile data was used to randomize/diversify cold code blocks to reduce overhead [Homescu et al, 2013], during profile-guided meta-programming [Bowman et al, 2015], to improve code cache management in JVMs [Robinson et al, 2016], to improve heap data locality in garbage collected runtimes [Huang et al, 2004], to guide object placement in partitioned hot/cold heaps to lower memory energy consumption [Jantz et al, 2015], etc. Our goal in this work is not to generate new or improve existing PGOs, but to determine how inaccuracy in profile data or static analysis based estimators can impact the effectiveness of PGOs.…”

Section: Background and Related Workmentioning

confidence: 99%

AOT vs. JIT: impact of profile data on code quality

Wade¹

2017

SIGPLAN Not.

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Dynamic or just-in-time (JIT) compilation can generate optimized native code during each program run, and is a popular technique used by process-level virtual machines (VM) to simultaneously achieve binary code portability and high execution performance. JIT compilers typically collect profile information at run-time to guide selective compilation and profile-guided optimizations (PGO). Ahead-of-time (AOT) compilation is an alternative model that can also achieve portability by converting the distributed binary to native code when the software is first installed. The AOT compilation model removes the overhead of online profile collection and dynamic compilation, and may reduce VM complexity. However, AOT compilation cannot customize the generated native code to different program inputs/behaviors. The goal of this work is to investigate and quantify the implications of the AOT compilation model on the quality of the generated native code for current VMs. To achieve our goal, this study develops and conducts many new innovative experiments using a variety of novel frameworks. First, we quantify the quality of native code generated by the two compilation models for a state-of-the-art (HotSpot) Java VM. Second, we determine how the amount of profile data collected affects the quality of generated code. Third, we develop a mechanism to determine the similarity or representative-ness of different profile data for a given program run for guiding PGOs.Fourth, we investigate how the accuracy of profile data affects its ability to effectively guide PGOs. Finally, we categorize the profile data types in our VM and explore the contribution of each such category to performance.iii

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Profile-guided automated software diversity

Cited by 93 publications

References 23 publications

Booby trapping software

Booby trapping software

High System-Code Security with Low Overhead

AOT vs. JIT: impact of profile data on code quality

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