No abstract

Code obfuscation is a major tool for protecting software intellectual property from attacks such as reverse engineering or code tampering. Yet, recently proposed (automated) attacks based on Dynamic Symbolic Execution (DSE) shows very promising results, hence threatening software integrity. Current defenses are not fully satisfactory, being either not efficient against symbolic reasoning, or affecting runtime performance too much, or being too easy to spot. We present and study a new class of anti-DSE protections coined as path-oriented protections targeting the weakest spot of DSE, namely path exploration. We propose a lightweight, efficient, resistant and analytically proved class of obfuscation algorithms designed to hinder DSE-based attacks. Extensive evaluation demonstrates that these approaches critically counter symbolic deobfuscation while yielding only a very slight overhead.

Abstract. Deduction modulo is a formalism introduced to separate cleanly computations and deductions by reasoning modulo a congruence on propositions. A sequent calculus modulo has been defined by Dowek, Hardin and Kirchner as well as a resolution-based proof search method called Extended Narrowing And Resolution (ENAR), in which the congruences are handled through rewrite rules on terms and atomic propositions. We define a tableau-based proof search method, called Tableau Method for Deduction modulo (TaMeD), for theorem proving modulo. We then give a syntactic proof of the completeness of the method with respect to provability in the sequent calculus modulo. Moreover, we follow in our proofs the same steps as the ENAR method in such a way that it allows to try and compare the characteristics of both methods.

Directed fuzzing focuses on automatically testing specific parts of the code by taking advantage of additional information such as (partial) bug stack trace, patches or risky operations. Key applications include bug reproduction, patch testing and static analysis report verification. Although directed fuzzing has received a lot of attention recently, hard-to-detect vulnerabilities such as Use-After-Free (UAF) are still not well addressed, more especially at the binary level. We propose UAFUZZ, the first (binary-level) directed greybox fuzzer dedicated to UAF bugs. The technique features a fuzzing engine tailored to UAF specifics, a lightweight code instrumentation and an efficient bug triage step. Experimental evaluation for bug reproduction on real cases demonstrates that UAFUZZ significantly outperforms state-of-the-art directed fuzzers in terms of fault detection rate, time to exposure and bug triaging. UAFUZZ has also been proven effective in patch testing, leading to the discovery of 20 new bugs in Perl, GPAC and GNU Patch (including a buggy patch) -all of them have been acknowledged and 14 have been fixed. Last but not least, we provide to the community the first fuzzing benchmark dedicated to UAF, built on both real codes and real bugs.