In this work we find that many current redactions of PDF text are insecure due to non-redacted character positioning information. In particular, subpixel-sized horizontal shifts in redacted and non-redacted characters can be recovered and used to effectively deredact first and last names. Unfortunately these findings affect redactions where the text underneath the black box is removed from the PDF. We demonstrate these findings by performing a comprehensive vulnerability assessment of common PDF redaction types. We examine 11 popular PDF redaction tools, including Adobe Acrobat, and find that they leak information about redacted text. We also effectively deredact hundreds of real-world PDF redactions, including those found in OIG investigation reports and FOIA responses. To correct the problem, we have released open source algorithms to fix vulnerable redactions and reduce the amount of information leaked by nonexcising redactions (where the text underneath the redaction is copy-pastable). We have also notified the developers of the studied redaction tools. We have notified the Office of Inspector General, the Free Law Project, PACER, Adobe, Microsoft, and the US Department of Justice. We are working with several of these groups to prevent our discoveries from being used for malicious purposes.

Multiple vendors have recently released SmartNICs that provide both special-purpose accelerators and programmable processing cores that allow increasingly sophisticated packet processing tasks to be offloaded from general-purpose CPUs. Indeed, leading data-center operators have designed and deployed SmartNICs at scale to support both network virtualization and application-specific tasks. Unfortunately, cloud providers have not yet opened up the full power of these devices to tenants, as current runtimes do not provide adequate isolation between individual applications running on the SmartNICs themselves.