Direct-to-consumer (DTC) genetic testing companies have provided personal genotyping services to millions of customers. Customers mail saliva samples to DTC service providers to have their genotypes analyzed and receive back their raw genetic data. Both consumers and the DTC companies use the results to perform ancestry analyses, relative matching, trait prediction, and estimate predisposition to disease, often relying on genetic databases composed of the data from millions of other DTC-genotyped individuals. While the digital integrity risks to this type of data have been explored, we considered whether data integrity issues could manifest upstream of data generation through physical manipulation of DNA samples themselves, for example by adding synthetic DNA to a saliva sample ("spiked samples") prior to sample processing by a DTC company. Here, we investigated the feasibility of this scenario within the standard DTC genetic testing pipeline. Starting with the purchase of off-the-shelf DTC genetic testing kits, we found that synthetic DNA can be used to precisely manipulate the results of saliva samples genotyped by a popular DTC genetic testing service and that this method can be used to modify arbitrary single nucleotide polymorphisms (SNPs) in multiplex to create customized doctored genetic profiles. This capability has implications for the use of DTC-generated results and the outcomes of their downstream analyses.
sequencing is the molecular-to-digital conversion of DNA molecules, which are made up of a linear sequence of bases (A,C,G,T), into digital information. Central to this conversion are specialized fluidic devices, called sequencing flow cells, that distribute DNA onto a surface where the molecules can be read. As more computing becomes integrated with physical systems, we set out to explore how sequencing flow cell architecture can affect the security and privacy of the sequencing process and downstream data analysis. In the course of our investigation, we found that the unusual nature of molecular processing and flow cell design contributes to two security and privacy issues. First, DNA molecules are `sticky' and stable for long periods of time. In a manner analogous to data recovery from discarded hard drives, we hypothesized that residual DNA attached to used flow cells could be collected and resequenced to recover a significant portion of the previously sequenced data. In experiments we were able to recover over 23.4% of a previously sequenced genome sample and perfectly decode image files encoded in DNA, suggesting that flow cells may be at risk of data recovery attacks. Second, we hypothesized that methods used to simultaneously sequence separate DNA samples together to increase sequencing throughput (multiplex sequencing), which incidentally leaks small amounts of data between samples, could cause data corruption and allow samples to adversarially manipulate sequencing data. We find that a maliciously crafted synthetic DNA sample can be used to alter targeted genetic variants in other samples using this vulnerability. Such a sample could be used to corrupt sequencing data or even be spiked into tissue samples, whenever untrusted samples are sequenced together. Taken together, these results suggest that, like many computing boundaries, the molecular-to-digital interface raises potential issues that should be considered in future sequencing and molecular sensing systems, especially as they become more ubiquitous.
Customers of direct-to-consumer (DTC) genetic testing services routinely download their raw genetic data and give it to third-party companies that support additional features. One type of analysis, called genetic genealogy, uses genetic data and genealogical methods to find new relatives. While genetic genealogy is quite popular, it has raised new privacy concerns. Genetic genealogy services can be leveraged to find the person corresponding to anonymous genetic data and have been used dozens of times by law enforcement to solve crimes. We hypothesized that the open design and broad API offered by some genetic genealogy services raise other significant security and privacy issues. To test this hypothesis, we analyzed the security practices of GEDmatch, the largest third-party genetic genealogy service. Here, we experimentally show how the GEDmatch API is vulnerable to a number of attacks from an adversary that only uploads normally formatted genetic data files and runs standard queries. Using a small number of specifically designed files and queries, an attacker can extract a large percentage of the genetic markers from other users; 92% of markers can be extracted with 98% accuracy, including hundreds of medically sensitive markers. We also find that an adversary can construct genetic data files that falsely appear like relatives to other samples in the database; in certain situations, these false relatives can be used to make the re-identification of genetic data more difficult. These attacks are possible because of the rich set of features supported by the API, including detailed visualizations, that are meant to enhance usability. We conclude with security recommendations for genetic genealogy services.
The rapid improvement in DNA sequencing has sparked a big data revolution in genomic sciences, which has in turn led to a proliferation of bioinformatics tools. To date, these tools have encountered little adversarial pressure. This paper evaluates the robustness of such tools if (or when) adversarial attacks manifest. We demonstrate, for the first time, the synthesis of DNA which – when sequenced and processed – gives an attacker arbitrary remote code execution. To study the feasibility of creating and synthesizing a DNA-based exploit, we performed our attack on a modified downstream sequencing utility with a deliberately introduced vulnerability. After sequencing, we observed information leakage in our data due to sample bleeding. While this phenomena is known to the sequencing community, we provide the first discussion of how this leakage channel could be used adversarially to inject data or reveal sensitive information. We then evaluate the general security hygiene of common DNA processing programs, and unfortunately, find concrete evidence of poor security practices used throughout the field. Informed by our experiments and results, we develop a broad framework and guidelines to safeguard security and privacy in DNA synthesis, sequencing, and processing.