Abstract:

With the widespread adoption of application softwares in intelligent systems, ensuring software security is crucial for enhancing the reliability of these systems. Although existing fuzz testing techniques can reveal software security vulnerabilities to some extent, they are often hindered by issues related to testing effectiveness and efficiency. To address these challenges, this paper proposed a mutation-sensitive fuzz testing method (Seq2Seq-Fuzzer). First, we introduced four Seq2Seq models based on improved LSTM and Transformer architectures, and trained the proposed models using byte vector datasets constructed from programs such as objdump, readelf, and others. Next, we appled the Seq2Seq model to optimize american fuzzy lop (AFL) by predicting effective mutation strategies and mutation position pairs, aiming to address the high randomness and low efficiency inherent in AFL fuzz testing. Finally, we evaluated the proposed AFL optimization method. Experimental results show that, in tests on objdump, readelf, and nm, the code coverage of Seq2Seq-Fuzzer surpasses that of AFL by up to 56.8%, and it successfully identifies 21 crashes in programs related to objdump.

Key words: intelligent systems, fuzz testing, Seq2Seq, Transformer, LSTM