We present counterfactual symbolic execution, a new approach that produces counterexamples that localize the causes of failure of static verification. First, we develop a notion of symbolic weak head normal form and use it to define lazy symbolic execution reduction rules for non-strict languages like Haskell. Second, we introduce counterfactual branching, a new method to identify places where verification fails due to imprecise specifications (as opposed to incorrect code). Third, we show how to use counterfactual symbolic execution to localize refinement type errors, by translating refinement types into assertions. We implement our approach in a new Haskell symbolic execution engine, G2, and evaluate it on a corpus of 7550 errors gathered from users of the LiquidHaskell refinement type system. We show that for 97.7% of these errors, G2 is able to quickly find counterexamples that show how the code or specifications must be fixed to enable verification. CCS Concepts • Theory of computation → Abstraction; • Computing methodologies → Symbolic and algebraic manipulation.

UNSTRUCTURED Health Level 7 (HL7) is a ubiquitous protocol in healthcare infrastructure, used to interface a variety of systems. HL7 lacks encryption at the level of the protocol, and is thus vulnerable to attacks that modify message contents. We describe a sophisticated man-in-the-middle attack that injects misinformation into clinical workflow that can cause patient harm.

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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.

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