PrescientFuzz: A Grey-Box Fuzzing Approach Leveraging Control Flow Graph Information to Improve Early Coverage Exploration

Keskeiset käsitteet

PrescientFuzz leverages the system under test's control flow graph information to guide the fuzzer towards inputs that are likely to discover more coverage when mutated, enabling earlier exploration of the program's functionality.

Tiivistelmä

The paper introduces PrescientFuzz, a grey-box fuzzing approach that aims to improve the early exploration of the system under test (SUT) by leveraging information from the SUT's control flow graph.

The key ideas are:

1. Direct Neighbours: PrescientFuzz identifies the set of basic blocks that are directly reachable from the blocks covered by the current input, but have not yet been covered by other inputs in the fuzzing queue. This information is used to prioritize inputs that can reach more uncovered blocks.

2. Reachable Blocks: PrescientFuzz extends the direct neighbour concept by considering all blocks that are reachable from the covered blocks without visiting any already covered blocks. This provides a more comprehensive view of the exploration potential of each input.

3. Rarity Weighting: To balance the fuzzing effort across different reachable blocks, PrescientFuzz assigns higher weights to inputs that can reach less frequently seen reachable blocks.

4. Depth Weighting: PrescientFuzz also considers the depth (number of conditional branches) required to reach each reachable block, prioritizing inputs that can reach more immediately accessible blocks.

The authors implemented PrescientFuzz using the LibAFL fuzzing framework and evaluated it against state-of-the-art fuzzers on the FuzzBench benchmark suite. The results show that PrescientFuzz outperformed other fuzzers on 5 out of the 11 benchmarks tested, demonstrating the effectiveness of their approach in improving early coverage exploration.

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Tärkeimmät oivallukset

PrescientFuzz: A more effective exploration approach for grey-box fuzzing

by Daniel Black... klo arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18887.pdf

Syvällisempiä Kysymyksiä

How could PrescientFuzz's control flow graph analysis be extended to also consider the semantic properties of the program, such as input constraints or complex conditional checks, to further guide the fuzzing process

PrescientFuzz's control flow graph analysis could be extended to consider the semantic properties of the program by incorporating data flow analysis. By analyzing how data propagates through the program, including input constraints and complex conditional checks, the fuzzer can make more informed decisions on which inputs to mutate. For instance, identifying data dependencies between variables and functions can help prioritize inputs that are likely to trigger specific paths in the program. Additionally, integrating symbolic execution techniques can assist in exploring different program paths based on symbolic inputs, allowing the fuzzer to target specific conditions or constraints more effectively. By combining control flow graph analysis with data flow and symbolic execution, PrescientFuzz can gain a deeper understanding of the program's behavior and make more intelligent choices during the fuzzing process.

What other types of program analysis, beyond control flow graphs, could be leveraged by grey-box fuzzers to improve their exploration capabilities

Beyond control flow graphs, grey-box fuzzers can leverage various program analysis techniques to enhance their exploration capabilities. One such technique is dynamic taint analysis, which tracks how data flows through the program and identifies tainted inputs that may influence security-critical operations. By prioritizing inputs that interact with tainted data, the fuzzer can focus on paths that are more likely to lead to vulnerabilities. Additionally, integrating static analysis methods like abstract interpretation can help identify potential program behaviors and guide the fuzzer towards exploring specific code regions. Furthermore, incorporating machine learning algorithms to predict which inputs are likely to uncover new paths based on historical data can improve the efficiency of the fuzzing process. By combining these diverse program analysis techniques, grey-box fuzzers can achieve more targeted and effective exploration of the program.

How could PrescientFuzz's techniques be adapted to work in a distributed fuzzing setup, where multiple instances of the fuzzer are running in parallel and sharing coverage information

Adapting PrescientFuzz's techniques for a distributed fuzzing setup involves coordinating multiple fuzzer instances to share coverage information and collaborate on the exploration process. One approach is to establish a centralized server that collects coverage data from each fuzzer instance and aggregates it to maintain a global view of the program's execution paths. Fuzzer instances can communicate their findings, such as newly discovered paths or interesting inputs, to the central server, which can then distribute this information to other instances for further exploration. Implementing a consensus mechanism to resolve conflicts and prioritize inputs based on collective knowledge can ensure efficient collaboration among distributed fuzzers. Additionally, incorporating load balancing strategies to allocate fuzzing tasks based on the current coverage status can optimize resource utilization and maximize coverage expansion across multiple instances. By enabling seamless communication and coordination among distributed fuzzer instances, PrescientFuzz's techniques can be effectively scaled to harness the collective intelligence of a distributed fuzzing environment.