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Paper Citation Record · LEDGER

A systematic review of fuzzing based on machine learning techniques

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:1908.01262.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.01262 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:21:57.476226Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

  • verified exact2
  • verified fuzzy18
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c8aae246-6353-4d71-aee5-cba1ac13a3dc · outbound

This paper cites V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing.

A systematic review of fuzzing based on machine learning techniques V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ae563068-3605-405e-9c36-8dc2844b80c3 · outbound

This paper cites Static and dynamic analysis: Synergy and duality.

A systematic review of fuzzing based on machine learning techniques Static and dynamic analysis: Synergy and duality

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a9ee15f4-38de-401d-8f6d-871925d36c6e · outbound

This paper cites word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method.

A systematic review of fuzzing based on machine learning techniques word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

Reference 14

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no resolver link, observed 2026-08-14T15:21:57.362441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3054615f-7424-487b-b5a7-3ebedcadc93c · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4719dc48-1818-40f3-8b50-30dcbf796410 · outbound

This paper cites Adaptive Grey-Box Fuzz-Testing with Thompson Sampling.

A systematic review of fuzzing based on machine learning techniques Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1b636f98-78ea-421b-bea8-43f735bc69da · outbound

This paper cites Pin: building customized program analysis tools with dynamic instrumentation.

A systematic review of fuzzing based on machine learning techniques Pin: building customized program analysis tools with dynamic instrumentation

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0203d76e-3d32-40d9-b4d7-74df685dd7df · outbound

This paper cites The NIST SARD project [Internet].

A systematic review of fuzzing based on machine learning techniques The NIST SARD project [Internet]

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 25f7c9a9-b998-462a-ba10-ebeb1cb6bfef · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 62aeb3f8-5c68-49c9-a568-e4772964b7fb · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5d7cf660-35ca-40d3-b86e-0756b8258f9c · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 28

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c0a57590-92df-4ebe-8ca0-d292f35e982c · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9e1a9f2f-e80a-4444-8f8c-48d242316ccc · outbound

This paper cites Body armor for binaries: preventing buffer overflows without recompilation.

A systematic review of fuzzing based on machine learning techniques Body armor for binaries: preventing buffer overflows without recompilation

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.436217Z digest=sha256:806023db614138bacf9d54346586be91613adbb80c3f5eafb438a5f40f07e364

Observation 82b37032-81da-4cd6-b1e1-02bd06bc21c1 · outbound

This paper cites Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump.

A systematic review of fuzzing based on machine learning techniques Exniffer: Learning to Prioritize Crashes by Assessing the Exploitability from Memory Dump

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.446571Z digest=sha256:b75bf09a3542ad1d420401f14ce24e7784cf6d1e8be188a0a4106e54f05e4ce6

Observation 3fb0696d-181a-4424-8dc5-8a3b544df612 · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5f7747bd-df19-45c8-bf7a-c8419838d34b · outbound

This paper cites Vulnerability detection with deep learning.

A systematic review of fuzzing based on machine learning techniques Vulnerability detection with deep learning

Reference 36

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.460188Z digest=sha256:a72d878471a6232240e644081f56de389ae162208f677bcaac1304aea220de1a

Observation 266822d8-0646-45a3-b1b8-6f473c660342 · outbound

This paper cites 1298–302.

A systematic review of fuzzing based on machine learning techniques 1298–302

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.464498Z digest=sha256:a695097fbd8e6b112f802bbc3a9cf239173fad80f5df65a471c7596eaee83586

Observation 2942ebbc-3d08-4f40-9cc4-2e3ad8fb8ad5 · outbound

This paper cites Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware.

A systematic review of fuzzing based on machine learning techniques Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:21:57.757551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation cf972b61-5a5d-4bc8-aa0c-c366b5523c9a · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 38cbc460-5634-432c-ae89-9a19c431ac9d · outbound

This paper cites Available from: http://lcamtuf.coredump.cx/afl/ Zhang G, Zhou X, Luo Y, Wu X, Min E.

A systematic review of fuzzing based on machine learning techniques Available from: http://lcamtuf.coredump.cx/afl/ Zhang G, Zhou X, Luo Y, Wu X, Min E

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3a1abbb6-136f-4627-9923-ce5dd41362c9 · outbound

This paper cites From automation to intelligence: Survey of research on vulnerability discovery techniques.

A systematic review of fuzzing based on machine learning techniques From automation to intelligence: Survey of research on vulnerability discovery techniques

Reference 41

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raw_fallback, observed 2026-08-14T15:21:57.588138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.476226Z digest=sha256:a2dcff98da7f230f5e209a80784bffd4fbc006e92c77a26852cc82d5a533ad2c

Observation 0eba6fee-088f-47d1-a2fe-3047fe0eaeac · outbound

This paper cites Fitness function [Internet]; 2019a [cited 2019 Jul 17].

A systematic review of fuzzing based on machine learning techniques Fitness function [Internet]; 2019a [cited 2019 Jul 17]

Reference 69

Resolution
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raw_fallback, observed 2026-08-14T15:21:57.674220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.455896Z digest=sha256:33a3d31926ae35567d5583628ea77af02978dde17172557de96989a236808215

Observation be191059-c22a-4d0b-866a-4d17c5b3fbdb · outbound

This paper cites SmartSeed: Smart Seed Generation for Efficient Fuzzing.

A systematic review of fuzzing based on machine learning techniques SmartSeed: Smart Seed Generation for Efficient Fuzzing

Reference 190

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:21:57.397837Z digest=sha256:298af191bc5f7ab6510aaa3d9e3781bcef1d67ba364e2e78287cb026e31dae51

Observation 8a961462-0b3c-48cb-a866-8910d5d34e78 · outbound

This paper cites Efficient backprop.

A systematic review of fuzzing based on machine learning techniques Efficient backprop

Reference 436

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2cf55fe4-8071-4212-ab8a-b05291d4f511 · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 1992

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 55eb3714-6b8e-4f16-b75f-1e994d25cff7 · outbound

This paper cites !exploitable [Internet].

A systematic review of fuzzing based on machine learning techniques !exploitable [Internet]

Reference 1998

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0131c391-0a65-4bb3-8225-263fb6ebc6fe · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2001

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9732c6b4-dcad-4837-9abe-18bd0fe4601b · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2003

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 78f5d573-29b2-4ad3-a8c5-f1bc2dc9c1e2 · outbound

This paper cites Unleashing Mayhem on Binary Code.

A systematic review of fuzzing based on machine learning techniques Unleashing Mayhem on Binary Code

Reference 2006

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e1520a98-2d1a-4e87-ba56-984bfb3dd9ed · outbound

This paper cites Big Code.

A systematic review of fuzzing based on machine learning techniques Big Code

Reference 2007

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4a4e6289-3629-4780-9082-65a5795a26ac · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2008

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2dbf1c17-cf7d-4e19-aee1-4cba018fad98 · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2009

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1a099f6c-1f68-4583-9828-18c1927f651d · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2010

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raw_fallback, observed 2026-08-14T15:21:58.076554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.295950Z digest=sha256:9ad30e09129e5fce966b929985081c1c487e83a3e6205e3d3018685a3cc32ea2

Observation f4f6d31d-fce8-4e8c-8ed2-e7f7d0aa967b · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2011

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raw_fallback, observed 2026-08-14T15:21:58.064132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.301107Z digest=sha256:5ec9a00e13a02300be83c7c467b25ecdd32167b1db7e5e7f4508e0685e0b7bda

Observation 014d810c-54e2-423e-8f04-78b9bdc419a5 · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2012

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raw_fallback, observed 2026-08-14T15:21:58.037971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.313611Z digest=sha256:0f7dbbc0f101154fc9e45cb86fb274dcd469d15f4f03508142a7ca2e4644b08f

Observation cbdf84d3-2769-4de7-ac3d-2313baf29e77 · outbound

This paper cites Available from: https://lcamtuf.blogspot.com/2014/08/binary-fuzzing-strategies-whatworks.html LeCun Y, Bengio Y, Hinton G.

A systematic review of fuzzing based on machine learning techniques Available from: https://lcamtuf.blogspot.com/2014/08/binary-fuzzing-strategies-whatworks.html LeCun Y, Bengio Y, Hinton G

Reference 2014

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:21:57.895975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.379393Z digest=sha256:d1b58312e966317723b0c414a94d69e518293b92c84b97713b5cc62160937376

Observation f87bdb39-7cb4-4a56-a8f6-34163b61bc7e · outbound

This paper cites Faster Fuzzing: Reinitialization with Deep Neural Models.

A systematic review of fuzzing based on machine learning techniques Faster Fuzzing: Reinitialization with Deep Neural Models

Reference 2015

Resolution
verified exact
local_arxiv, observed 2026-08-14T15:21:57.517137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.406450Z digest=sha256:b228bbc80865acbd24affeb09ea7523d19ac659cb3ea3d28263845294589190b

Observation e4139b1a-7b0c-4cc3-9b7f-9cfca9f7684e · outbound

This paper cites FuzzerGym: A Competitive Framework for Fuzzing and Learning.

A systematic review of fuzzing based on machine learning techniques FuzzerGym: A Competitive Framework for Fuzzing and Learning

Reference 2016

Resolution
verified exact
local_arxiv, observed 2026-08-14T15:21:57.572769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.337844Z digest=sha256:5dd32dbcd5ce4d8159c7b2db1f59a58e1d3c4e744e74b417ff519caf074761d9

Observation ac6a9300-64fe-4574-9bd8-42ce473b63ea · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2017

Resolution
unresolved
raw_fallback, observed 2026-08-14T15:21:57.961457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.354574Z digest=sha256:07817fcff2e8108803b569970fbfdb6de6b66f4304ca1902287a0a47252fa731

Observation 5e214198-5680-4726-b8a2-8701e56a7460 · outbound

This paper cites an unresolved cited work.

A systematic review of fuzzing based on machine learning techniques Unresolved cited work

Reference 2018

Resolution
unresolved
raw_fallback, observed 2026-08-14T15:21:58.025368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.323655Z digest=sha256:30fd8cedb22272dbfe22aff95dc370d295f984f3de8ee42965e9437d1396c86b

Observation d222394c-f324-4cae-9fd5-61dc4fca8d1c · outbound

This paper cites AEG: Automatic exploit generation.

A systematic review of fuzzing based on machine learning techniques AEG: Automatic exploit generation

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:21:58.089231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T15:21:57.291155Z digest=sha256:7b4c554a4e6bd39f76379ba8f0e1c49e9bd1953a09bacbda617ff1574b5faae9

Pith citing papers

No inbound Pith citation observations are available.