Coverage-guided fuzzing is a widely used and effective solution to find software vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to coverageguided fuzzers. However, tracking full and accurate path coverage is infeasible in practice due to the high instrumentation overhead. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. First, it causes path collisions, which prevent fuzzers from discovering potential paths that lead to new crashes. More importantly, it prevents fuzzers from making wise decisions on fuzzing strategies.In this paper, we propose a coverage sensitive fuzzing solution CollAFL. It mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead. It also utilizes the coverage information to apply three new fuzzing strategies, promoting the speed of discovering new paths and vulnerabilities. We implemented a prototype of CollAFL based on the popular fuzzer AFL and evaluated it on 24 popular applications. The results showed that path collisions are common, i.e., up to 75% of edges could collide with others in some applications, and CollAFL could reduce the edge collision ratio to nearly zero. Moreover, armed with the three fuzzing strategies, CollAFL outperforms AFL in terms of both code coverage and vulnerability discovery. On average, CollAFL covered 20% more program paths, found 320% more unique crashes and 260% more bugs than AFL in 200 hours. In total, CollAFL found 157 new security bugs with 95 new CVEs assigned.1 SanitizerCoverage claims supporting edge coverage in its documentation. But it is just an enhanced version of block coverage. More details will be discussed in Section II-C.

Compared with carbon nanotubes and graphene, graphene oxide (GO) exhibits excellent water solubility and biocompatibility in addition to the characteristic G band in Raman spectra. Therefore GO might be able to act as a flexible Raman probe to image cells or tissues through Raman mapping. However, the weak intensity of the G band restricts such applications of GO. Here we decorated GO with Au nanoparticles and found that the Raman intensity of GO in aqueous dispersions were remarkably enhanced by the surface enhancement effect. Therefore, rapid Raman imaging for Hela 229 cells was realized using Au/GO hybrids as Raman probes. The cell internalization mechanism of GO and Au/GO hybrids were also studied using Raman imaging. An endocytosis pathway was proposed from the results. In addition, the aqueous dispersions of Au/GO hybrids are stable for several weeks. Therefore, relying on the surface enhancement effect of Au nanoparticles, GO exhibits great potential as a general Raman imaging tool for biosystems.

The growth of semiconducting single-walled carbon nanotubes (s-SWNTs) on flat substrates is essential for the application of SWNTs in electronic and optoelectronic devices. We developed a flexible strategy to selectively grow s-SWNTs on silicon substrates using a ceria-supported iron or cobalt catalysts. Ceria, which stores active oxygen, plays a crucial role in the selective growth process by inhibiting the formation of metallic SWNTs via oxidation. The so-produced ultralong s-SWNT arrays are immediately ready for building field effect transistors.