Secure program execution via dynamic information flow tracking

By Edward Suh, Jaewoo Lee, Srini Devadas, David Zhang. ASPLOS XI, 2004

864 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12b48caf309169dc69d1b2e25a49c0cf6077cbf3

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Citations in the corpus

All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask) (2010, positive): “Due to the high overhead of binary instrumentation techniques, more efficient compiler-based [41, 63] and hardware-based [24, 25, 58, 59] approaches were later proposed.”
LIFT: A Low-Overhead Practical Information Flow Tracking System for Detecting Security Attacks (2006, methodology): “Information flow tracking systems can be implemented in three different ways, including language-based approaches [18, 19, 24, 31, 32], software-only approaches either in binary level [11, 14, 34] or in source level [42], or hardware extensions [17, 36, 39].”
A Survey of Malware Detection Techniques (2007, methodology): “in [45] propose a method which would require modification to the Oper-”
Vigilante: end-to-end containment of internet worms (2005, methodology): “namic dataflow analysis [43, 11] perform well but they have not been implemented yet and they lack flexibility (for example, they cannot track the origin of dirty data to aid SCA generation).”
Taint-Enhanced Policy Enforcement: A Practical Approach to Defeat a Wide Range of Attacks (2006, methodology): “The key distinctions between our work and previous fine-grained taint analysis techniques of [22, 28, 5] were already discussed in the introduction, so we limit our discussion to the more technical points here.”
Argos: an emulator for fingerprinting zero-day attacks for advertised honeypots with automatic signature generation (2006, background): “Similarly, [17] uses dynamic information flow analysis to guard against overflows”
Cross Site Scripting Prevention with Dynamic Data Tainting and Static Analysis (2007, background): “Likewise, if a property of an object is set to a tainted value, then the whole object needs to be tainted.”
Automatically patching errors in deployed software (2009, background): “Dynamic taint analysis finds appearances of potentially malicious data in sensitive locations such as function pointers or return addresses [46, 11, 29].”
Polyglot: automatic extraction of protocol message format using dynamic binary analysis (2007, methodology): “The execution monitor implements dynamic taint analysis [13, 14, 15, 28, 34, 35].”
Dynamic Taint Analysis for Automatic Detection, Analysis, and SignatureGeneration of Exploits on Commodity Software (2005, background): “independently and concurrently proposed a hardware extension to detect attacks by detecting when data from untrusted inputs is used as instructions or jump target addresses [52].”
Paragraph: Thwarting Signature Learning by Training Maliciously (2006, positive): “In the case of automatic generation of worm signatures, host monitoring techniques such as dynamic taint analysis [16, 4, 23, 3] can prevent such mislabeling, as they reliably detect whether the sample actually results in software being exploited.”
Limits of Learning-based Signature Generation with Adversaries (2008, background): “It holds even if host-monitoring techniques like taint analysis [9,11,27,30] are used to identify exact ly which packets are malicious and which are not.”
Aegis: A Single-Chip Secure Processor (2007, background): “Dynamic information flow tracking [137] protects programs against malicious software attacks by identifying spurious information flows from untrusted I/O, tracking the flows at run-time, and restricting their usage.”
BitBlaze: A New Approach to Computer Security via Binary Analysis (2008, methodology): “For example, previous approaches either operate on a single process only [17,35,38], or they cannot deal with memory swapping and disks [16, 18].”
An empirical study of privacy-violating information flows in JavaScript web applications (2010, background): “Several authors have investigated the use of dynamic taint propagation and checking, using specialized hardware [27, 29], virtual machines [4], binary rewriting [22], and sourcelevel rewriting [5, 19].”