With the widespread deployment of Control-Flow Integrity (CFI), control-flow hijacking attacks, and consequently code reuse attacks, are significantly more difficult. CFI limits control flow to well-known locations, severely restricting arbitrary code execution. Assessing the remaining attack surface of an application under advanced control-flow hijack defenses such as CFI and shadow stacks remains an open problem.
We introduce BOPC, a mechanism to automatically assess whether an attacker can execute arbitrary code on a binary hardened with CFI/shadow stack defenses. BOPC computes exploits for a target program from payload specifications written in a Turing-complete, high-level language called SPL that abstracts away architecture and program-specific details. SPL payloads are compiled into a program trace that executes the desired behavior on top of the target binary. The input for BOPC is an SPL payload, a starting point (e.g., from a fuzzer crash) and an arbitrary memory write primitive that allows application state corruption. To map SPL payloads to a program trace, BOPC introduces Block Oriented Programming (BOP), a new code reuse technique that utilizes entire basic blocks as gadgets along valid execution paths in the program, i.e., without violating CFI or shadow stack policies. We find that the problem of mapping payloads to program traces is NP-hard, so BOPC first reduces the search space by pruning infeasible paths and then uses heuristics to guide the search to probable paths. BOPC encodes the BOP payload as a set of memory writes.
We execute 13 SPL payloads applied to 10 popular applications. BOPC successfully finds payloads and complex execution traces ś which would likely not have been found through manual analysis ś while following the target’s Control-Flow Graph under an ideal CFI policy in 81% of the cases.
more »
« less
TestLocal: just-in-time parametrized testing of local variables
Variables local to methods play an important role in implementing not only the logic of desired algorithms but also the control of global variables. The values of these variables dictate the paths to be taken while executing the program. While testing is an effective way to exercise the programs under test, the values of these local variables determines the reachability of execution paths. Hence, it is possible that these local variables control the execution of the program and thus may result in some paths never to be tested adequately.This paper introduces a methodology and a prototype tool, called“TestLocal”, to enable testing of methods by changing the values of local variables at runtime thus enabling traversing the paths that are hard to execute. By changing the values of local variables during the execution, the unexpected paths are intentionally forcedto be executed. In comparison with the existing approaches (e.g.,Microsoft IntelliTest), the proposed methodology requires little to no modifications to the given code. The novelty of the proposedapproach is that it integrates a constraint solver and a debugger to forcefully execute unreachable paths.
more »
« less
- Award ID(s):
- 1821560
- NSF-PAR ID:
- 10186765
- Date Published:
- Journal Name:
- SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
- Page Range / eLocation ID:
- 1874 to 1877
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we the present conditions on a probabilistic program’s execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m -consumed property and the unseparated paths property . A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m -consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.more » « less
-
As control-flow protection techniques are widely deployed, it is difficult for attackers to modify control data, like function pointers, to hijack program control flow. Instead, data-only attacks corrupt security-critical non-control data (critical data), and can bypass all control-flow protections to revive severe attacks. Previous works have explored various methods to help construct or prevent data-only attacks. However, no solution can automatically detect program-specific critical data. In this paper, we identify an important category of critical data, syscall-guard variables, and propose a set of solutions to automatically detect such variables in a scalable manner. Syscall-guard variables determine to invoke security-related system calls (syscalls), and altering them will allow attackers to request extra privileges from the operating system. We propose branch force, which intentionally flips every conditional branch during the execution and checks whether new security-related syscalls are invoked. If so, we conduct data-flow analysis to estimate the feasibility to flip such branches through common memory errors. We build a tool, VIPER, to implement our ideas. VIPER successfully detects 34 previously unknown syscall-guard variables from 13 programs. We build four new data-only attacks on sqlite and v8, which execute arbitrary command or delete arbitrary file. VIPER completes its analysis within five minutes for most programs, showing its practicality for spotting syscall-guard variables.more » « less
-
null (Ed.)Intermittently-powered, energy-harvesting devices operate on energy collected from their environment and must operate intermittently as energy is available. Runtime systems for such devices often rely on checkpoints or redo-logs to save execution state between power cycles, causing arbitrary code regions to re-execute on reboot. Any non-idempotent program behavior—behavior that can change on each execution—can lead to incorrect results. This work investigates non-idempotent behavior caused by repeating I/O operations, not addressed by prior work. If such operations affect a control statement or address of a memory update, they can cause programs to take different paths or write to different memory locations on re-executions, resulting in inconsistent memory states. We provide the first characterization of input-dependent idempotence bugs and develop IBIS-S, a program analysis tool for detecting such bugs at compile time, and IBIS-D, a dynamic information flow tracker to detect bugs at runtime. These tools use taint propagation to determine the reach of input. IBIS-S searches for code patterns leading to inconsistent memory updates, while IBIS-D detects concrete memory inconsistencies. We evaluate IBIS on embedded system drivers and applications. IBIS can detect I/O-dependent idempotence bugs, giving few (IBIS-S) or no (IBIS-D) false positives and providing actionable bug reports. These bugs are common in sensor-driven applications and are not fixed by existing intermittent systems.more » « less
-
Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants. This paper presents HyDiff, the first hybrid approach for differential software analysis. HyDiff integrates and extends two very successful testing techniques: Feedback-directed greybox fuzzing for efficient program testing and shadow symbolic execution for systematic program exploration. HyDiff extends greybox fuzzing with divergence-driven feedback based on novel cost metrics that take into account the control flow graph of the program. Furthermore HyDiff extends shadow symbolic execution by applying four-way forking in a systematic exploration and still having the ability to incorporate concrete inputs in the analysis. HyDiff applies divergence revealing heuristics based on resource consumption and control-flow information to efficiently guide the symbolic exploration, which allows its efficient usage beyond regression testing applications. We introduce differential metrics such as output, decision and cost difference, as well as patch distance, to assist the fuzzing and symbolic execution components in maximizing the execution divergence. We implemented our approach on top of the fuzzer AFL and the symbolic execution framework Symbolic PathFinder. We illustrate HyDiff on regression and side-channel analysis for Java bytecode programs, and further show how to use HyDiff for robustness analysis of neural networks.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3297280.3297613
