We present an automated approach for detecting and quantifying side channels in Java programs, which uses symbolic execution, string analysis and model counting to compute information leakage for a single run of a program. We further extend this approach to compute information leakage for multiple runs for a type of side channels called segmented oracles, where the attacker is able to explore each segment of a secret (for example each character of a password) independently. We present an efficient technique for segmented oracles that computes information leakage for multiple runs using only the path constraints generated from a single run symbolic execution. Our implementation uses the symbolic execution tool Symbolic PathFinder (SPF), SMT solver Z3, and two model counting constraint solvers LattE and ABC. Although LattE has been used before for analyzing numeric constraints, in this paper, we present an approach for using LattE for analyzing string constraints. We also extend the string constraint solver ABC for analysis of both numeric and string constraints, and we integrate ABC in SPF, enabling quantitative symbolic string analysis. CCS Concepts •Software and its engineering → Formal software verification; •Security and privacy → Logic and verification;
We present symbolic analysis techniques for detecting vulnerabilities that are due to adaptive side-channel attacks, and synthesizing inputs that exploit the identified vulnerabilities. We start with a symbolic attack model that encodes succinctly all the side-channel attacks that an adversary can make. Using symbolic execution over this model, we generate a set of mathematical constraints, where each constraint characterizes the set of secret values that lead to the same sequence of sidechannel measurements. We then compute the optimal attack, i.e, the attack that yields maximum leakage over the secret, by solving an optimization problem over the computed constraints. We use information-theoretic concepts such as channel capacity and Shannon entropy to quantify the leakage over multiple runs in the attack, where the measurements over the side channels form the observations that an adversary can use to try to infer the secret. We also propose greedy heuristics that generate the attack by exploring a portion of the symbolic attack model in each step. We implemented the techniques in Symbolic PathFinder and applied them to Java programs encoding web services, string manipulations and cryptographic functions, demonstrating how to synthesize optimal side-channel attacks.
Recent automated software testing techniques concentrate on achieving path coverage. We present a complexity measure that provides an upper bound for the number of paths in a program, and hence, can be used for assessing the difficulty of achieving path coverage for a given method. We define the path complexity of a program as a function that takes a depth bound as input and returns the number of paths in the control flow graph that are within that bound. We show how to automatically compute the path complexity function in closed form, and the asymptotic path complexity which identifies the dominant term in the path complexity function. Our results demonstrate that path complexity can be computed efficiently, and it is a better complexity measure for path coverage compared to cyclomatic complexity and NPATH complexity.
We present a black-box, dynamic technique to detect and quantify side-channel information leaks in networked applications that communicate through a TLS-encrypted stream. Given a user-supplied profiling-input suite in which some aspect of the inputs is marked as secret, we run the application over the inputs and capture a collection of variable-length network packet traces. The captured traces give rise to a vast side-channel feature space, including the size and timestamp of each individual packet as well as their aggregations (such as total time, median size, etc.) over every possible subset of packets. Finding the features that leak the most information is a difficult problem. Our approach addresses this problem in three steps: 1) Global analysis of traces for their alignment and identification of phases across traces; 2) Feature extraction using the identified phases; 3) Information leakage quantification and ranking of features via estimation of probability distribution. We embody this approach in a tool called Profit and experimentally evaluate it on a benchmark of applications from the DARPA STAC program, which were developed to assess the effectiveness of side-channel analysis techniques. Our experimental results demonstrate that, given suitable profiling-input suites, Profit is successful in automatically detecting information-leaking features in applications, and correctly ordering the strength of the leakage for differently-leaking variants of the same application.
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