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Paper Citation Record · LEDGER

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution

As of 14 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2607.22076.

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

pith.paper-citation-record.v1
2607.22076 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:55:25.661951Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

63 of 63 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved62
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5545d04b-6b99-409e-9122-15a8fc9f751f · outbound

This paper cites What are cves? understanding common vul- nerabilities,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution What are cves? understanding common vul- nerabilities,

Reference 1

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source=pdf_text observed=2026-08-01T05:55:20.242618Z digest=sha256:f69641a6aa3f3193271ee22252b8ba7acebc09de1349456139ebd93f02b16fcd

Observation 1b329e87-7083-47ff-bb7f-a8b555479372 · outbound

This paper cites A large-scale empirical study of security patches,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution A large-scale empirical study of security patches,

Reference 2

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source=pdf_text observed=2026-08-01T05:55:20.318721Z digest=sha256:bc029f0e6e3e1e7947a2d0f883f695ee9b9b3c2845733231d4a88575b151fcf3

Observation d90f7aa8-e4a6-48da-8059-4a19b121093f · outbound

This paper cites Detecting.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Detecting

Reference 3

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source=pdf_text observed=2026-08-01T05:55:20.404073Z digest=sha256:33d523085dc68545094e1f3bb59f0ebf174bcb63d884c70d5533df5f41d51196

Observation d92aeed5-ea93-420c-bd17-dffd29899ed3 · outbound

This paper cites Finding a needle in a haystack: Automated mining of silent vulnerability fixes,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Finding a needle in a haystack: Automated mining of silent vulnerability fixes,

Reference 4

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source=pdf_text observed=2026-08-01T05:55:20.525228Z digest=sha256:2bb6504cf78f27a533417acfa5eb53b665886298063fee897aa4ca6460231699

Observation 6c5e3ba5-cb77-4e20-b582-64e13e3e79c2 · outbound

This paper cites What lies beneath: an empirical study of silent vulnerability fixes in open-source software,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution What lies beneath: an empirical study of silent vulnerability fixes in open-source software,

Reference 5

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source=pdf_text observed=2026-08-01T05:55:20.649928Z digest=sha256:f4fe31e44db9bd928e8e1924b939e65e049fd91a66aca6ec1c545ab5ce40a4a2

Observation 2c4fcb81-2127-44ff-be30-76cdb5c2aa44 · outbound

This paper cites Automatic patch- based exploit generation is possible: Techniques and implications,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Automatic patch- based exploit generation is possible: Techniques and implications,

Reference 6

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source=pdf_text observed=2026-08-01T05:55:20.767571Z digest=sha256:2a4fa6a30d68e3924ba0797bf8205fd18dafaa3a7aac7f519f522d8b8420e2e2

Observation c29e7c8b-fa0c-488d-b118-aa01d5f3c427 · outbound

This paper cites Scaling javascript abstract interpretation to detect and exploit node. js taint-style vulnerability,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Scaling javascript abstract interpretation to detect and exploit node. js taint-style vulnerability,

Reference 7

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source=pdf_text observed=2026-08-01T05:55:20.886540Z digest=sha256:48acb40b4c4cf84be95bbf8ab1d87f5eaa74a53b5faf81ba0e01a75e4cb42cfc

Observation 8c3c1170-ea0c-4e63-ace4-cbe7e59bd07b · outbound

This paper cites Automated exploit generation for node. js packages,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Automated exploit generation for node. js packages,

Reference 8

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source=pdf_text observed=2026-08-01T05:55:21.052251Z digest=sha256:0db5b3cac5bca4d29732805d1797c32a9f8c19d8bd189dd5a4110688e0fa9d5c

Observation bcb97fa2-df47-48eb-ba37-0e71cdfce107 · outbound

This paper cites Nodemedic- fine: Automatic detection and exploit synthesis for node. js vulnerabili- ties.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Nodemedic- fine: Automatic detection and exploit synthesis for node. js vulnerabili- ties

Reference 9

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source=pdf_text observed=2026-08-01T05:55:21.197979Z digest=sha256:e2ff5da4fe958b6543064559124807d1b1463f5f326d681762e3cca7c8978bf1

Observation 3133b505-ff75-41bd-8b65-d55611a628fa · outbound

This paper cites Pocgen: Generating proof- of-concept exploits for vulnerabilities in npm packages,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Pocgen: Generating proof- of-concept exploits for vulnerabilities in npm packages,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-01T05:55:21.370547Z digest=sha256:fc28e2a4266a24c6ceebe9d7bd79e0c90c7012020b186f10b3a561552c30131b

Observation e7c23847-5a17-4efc-b80b-c0f6bebb443b · outbound

This paper cites Code change intention, development artifact, and history vulnerability: Putting them together for vulnerability fix detection by llm,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Code change intention, development artifact, and history vulnerability: Putting them together for vulnerability fix detection by llm,

Reference 11

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source=pdf_text observed=2026-08-01T05:55:21.509723Z digest=sha256:9ed0041b67d639158d455964f4f057ef418780132549194ee8266aad4fae5714

Observation bf0ed59c-501e-4f9e-bdfd-c25ccc729eee · outbound

This paper cites Not every patch is an island: Llm-enhanced identification of multiple vulnerability patches,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Not every patch is an island: Llm-enhanced identification of multiple vulnerability patches,

Reference 12

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source=pdf_text observed=2026-08-01T05:55:21.619545Z digest=sha256:a9f4215ba49ef14b879dbae124e29af98fed28ef08c5c927df736090e4e66d75

Observation 192f7fe1-2be6-4b35-b827-f148f70c9f4f · outbound

This paper cites CVE List V5,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution CVE List V5,

Reference 13

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source=pdf_text observed=2026-08-01T05:55:21.750261Z digest=sha256:c869eff599e1f9973c4abf4e67a72a5b88662359f8bd286874c8aec02cd46654

Observation 71ed3bc8-5e14-4185-a9ec-3004aa1e1ba3 · outbound

This paper cites Large language models are human-level prompt engineers,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Large language models are human-level prompt engineers,

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-01T05:55:21.854356Z digest=sha256:f3b2ceef2e1554eb03ef9db6ab57bd08e6b0e1502f74f6071501eaca26c23af1

Observation 717b93ee-c1df-4bc9-8a9d-bbdebf84b52f · outbound

This paper cites Promptbreeder: Self-referential self-improvement via prompt evolution,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Promptbreeder: Self-referential self-improvement via prompt evolution,

Reference 15

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source=pdf_text observed=2026-08-01T05:55:22.009010Z digest=sha256:e5d2b9c25b3a4c712e2ddf9635afb721bd454f7afa298cda52aef87f750c847d

Observation b693bbbf-e0c2-4d84-a8d2-3aacd3725838 · outbound

This paper cites Self-refine: Iter- ative refinement with self-feedback,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Self-refine: Iter- ative refinement with self-feedback,

Reference 16

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source=pdf_text observed=2026-08-01T05:55:22.170904Z digest=sha256:c8f21e492dbeeb584ec16011d2b3616ce1946164b5d634be25e3d20a412e1dde

Observation a637a3be-609f-4d71-90b4-b589687957d3 · outbound

This paper cites Re- flexion: Language agents with verbal reinforcement learning,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Re- flexion: Language agents with verbal reinforcement learning,

Reference 17

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source=pdf_text observed=2026-08-01T05:55:22.293050Z digest=sha256:8bceee3178c8c80b0e7de8e63b41bcceb309d3906da1494a9675a3949fbb839c

Observation 3667c861-4ad8-4301-a22d-0c732c4a110a · outbound

This paper cites Automatic prompt optimization with “gradient descent.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Automatic prompt optimization with “gradient descent

Reference 18

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source=pdf_text observed=2026-08-01T05:55:22.443804Z digest=sha256:d37f7b2122e5dd23cb2dff87becf737f0b1e635aaa498c82810999ac0b14b859

Observation 2cbce5d9-51f8-46b7-80c1-f86f61a4c649 · outbound

This paper cites Critic: Large language models can self-correct with tool-interactive critiquing,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Critic: Large language models can self-correct with tool-interactive critiquing,

Reference 19

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source=pdf_text observed=2026-08-01T05:55:22.599875Z digest=sha256:c389adea8e2b7cb531cac254c341fca6447dae0f9ae3f2e3bbfa496e98a4b711

Observation ac921e53-8c4d-400a-affd-c7e8e8d2f5a4 · outbound

This paper cites Large language models as optimizers,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Large language models as optimizers,

Reference 20

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source=pdf_text observed=2026-08-01T05:55:22.766439Z digest=sha256:819bb13465aa9b618d2253bc4272abf97c49dd3a8c38f93281d0457e6fdb5b5f

Observation 66d66c19-a865-490d-9772-83be0f2ef6a5 · outbound

This paper cites GEPA: Reflective prompt evolution can outperform reinforcement learning,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution GEPA: Reflective prompt evolution can outperform reinforcement learning,

Reference 21

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source=pdf_text observed=2026-08-01T05:55:22.927586Z digest=sha256:b0ec0bd318e46c7b7ad193ce788072e4a18a72efe2d8ebde91d86487e8739900

Observation 7899ad22-9fc5-43f0-bb4d-a71b8ff8d5a5 · outbound

This paper cites dirty-json,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution dirty-json,

Reference 22

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source=pdf_text observed=2026-08-01T05:55:23.011703Z digest=sha256:37a4927c9bf661f2a6c52168d47e2559391ec7b51a2cba60eb15e9115c8eb2b8

Observation ca99861b-55d9-484e-b6f6-730db2246937 · outbound

This paper cites dirty-json Vulnerability: Prototype Pollution,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution dirty-json Vulnerability: Prototype Pollution,

Reference 23

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source=pdf_text observed=2026-08-01T05:55:23.016212Z digest=sha256:c4e4a8cd87a3ef1c650845f54ea979be956c49d1636318e33a7834fde961b387

Observation 36caa297-e6a0-428a-82e8-cc988e67fafb · outbound

This paper cites SSPCatcher: Learning to catch security patches,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution SSPCatcher: Learning to catch security patches,

Reference 24

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source=pdf_text observed=2026-08-01T05:55:23.021244Z digest=sha256:28a922480de3a4cf66e243d888d75e55670658beae888f54a81411d38803c1c2

Observation 4b277036-8175-476d-ae4e-5136a417ac9f · outbound

This paper cites VulCurator: A vulnerability-fixing commit detector,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution VulCurator: A vulnerability-fixing commit detector,

Reference 25

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source=pdf_text observed=2026-08-01T05:55:23.025556Z digest=sha256:dd9875d87bba7ec7da530d6e954d3cbc2640c8f35f0741c0da353ea15dda6218

Observation 38f6196f-4ab7-4a96-aea3-f12fe128e439 · outbound

This paper cites SPI: Automated identification of security patches via commits,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution SPI: Automated identification of security patches via commits,

Reference 26

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source=pdf_text observed=2026-08-01T05:55:23.030368Z digest=sha256:96ec544ea63f99a41f8ab92934041ddee780ea6f83766108d63559d080faf518

Observation b139bb4f-c16c-40cf-95e5-20bb2fcf734f · outbound

This paper cites Semfuzz: Semantics-based automatic generation of proof-of-concept exploits,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Semfuzz: Semantics-based automatic generation of proof-of-concept exploits,

Reference 27

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source=pdf_text observed=2026-08-01T05:55:23.034613Z digest=sha256:08ac7b37d195248f77f81b33727698b84058c587df346a26ed15235f619dd0fe

Observation 49137a31-7ec6-4d55-8e55-33669351225b · outbound

This paper cites Exploiting library vulnerability via migration based automating test generation,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Exploiting library vulnerability via migration based automating test generation,

Reference 28

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source=pdf_text observed=2026-08-01T05:55:23.039169Z digest=sha256:2a774b7b2611147a496e18aa45339e3b7cced1277549fb5ae9c50ef2066bef5f

Observation 7885e386-a05b-4e6e-aa30-369edc0b0544 · outbound

This paper cites Test mimicry to assess the exploitability of library vulnerabilities,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Test mimicry to assess the exploitability of library vulnerabilities,

Reference 29

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source=pdf_text observed=2026-08-01T05:55:23.100397Z digest=sha256:2ad8b6ad9dfadd10d2a641b99559e04127867b435c290eec83c8dd24b7ea1de9

Observation 2f11adeb-b05c-440d-beaa-1e73ae18d1c8 · outbound

This paper cites {APPATCH}: Automated adaptive prompting large language models for{Real-World} software vulnerability patching,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution {APPATCH}: Automated adaptive prompting large language models for{Real-World} software vulnerability patching,

Reference 30

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source=pdf_text observed=2026-08-01T05:55:23.219244Z digest=sha256:764919eda53a2f365b05bb9b830bfb1315f802839ad0b8615cd1834570535d4e

Observation bb2ee55f-d0eb-43d0-9dca-ddbe13e1f382 · outbound

This paper cites Docter: Documentation-guided fuzzing for testing deep learning api functions,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Docter: Documentation-guided fuzzing for testing deep learning api functions,

Reference 31

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source=pdf_text observed=2026-08-01T05:55:23.352716Z digest=sha256:dba98ccbf89850afd842dfd6794013dbed4ef673020e71590002a15823ab6916

Observation c9068e9f-fc5e-4b5b-9aea-7a03c0a7c2b5 · outbound

This paper cites {CarpetFuzz}: Automatic program option constraint extraction from documentation for fuzzing,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution {CarpetFuzz}: Automatic program option constraint extraction from documentation for fuzzing,

Reference 32

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source=pdf_text observed=2026-08-01T05:55:23.478859Z digest=sha256:97ed8b2cf080e83476e20b15c498ac3fe49cdb128a395be07f50a780ab9b32e1

Observation ef61abc9-a0b2-4e52-a6e5-95cffc2e9c78 · outbound

This paper cites Learning from the past: Real-world exploit migration for smart contract poc generation,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Learning from the past: Real-world exploit migration for smart contract poc generation,

Reference 33

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source=pdf_text observed=2026-08-01T05:55:23.578193Z digest=sha256:c827402327649a945cea0cc75769283560e6918f72612c50ad27a80b5857be82

Observation b8e90817-bee4-4941-b914-aa2d3efa81f0 · outbound

This paper cites Language models are few-shot learners,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Language models are few-shot learners,

Reference 34

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source=pdf_text observed=2026-08-01T05:55:23.676744Z digest=sha256:b5973c65840a1054b9b57a74798afde6c9eb145fae840833a539d20b41081db1

Observation fe7ba958-eb62-4fc6-a8f7-77af26c43028 · outbound

This paper cites {ChainFuzz}: Exploiting upstream vulnerabilities in{Open-Source}supply chains,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution {ChainFuzz}: Exploiting upstream vulnerabilities in{Open-Source}supply chains,

Reference 35

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source=pdf_text observed=2026-08-01T05:55:23.844702Z digest=sha256:f89a02f6338e4ffa0bd43e1c42495c59a24413d3dd25cdea995f8320bf48061c

Observation 7877b147-52c7-4a4c-b192-f4ec33aace32 · outbound

This paper cites {NA VEX}: Precise and scalable exploit generation for dynamic web applications,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution {NA VEX}: Precise and scalable exploit generation for dynamic web applications,

Reference 36

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source=pdf_text observed=2026-08-01T05:55:23.972782Z digest=sha256:5d4b5f37acc3ac867cc0be78a0c4ba8cdd36c0233b33613bf445afcc372ac9bf

Observation 4c76db4e-24ca-4837-bd06-9e2aa69cd86d · outbound

This paper cites Revery: From proof-of-concept to exploitable,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Revery: From proof-of-concept to exploitable,

Reference 37

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source=pdf_text observed=2026-08-01T05:55:24.058916Z digest=sha256:cd32d7d255be56abd2655b64ee08e6da705a1a652268541a55aa8cc004d121f2

Observation 270df044-54e4-49e5-8272-15d4ae29fba9 · outbound

This paper cites 1dfuzz: Reproduce 1-day vulnerabilities with directed differ- ential fuzzing,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution 1dfuzz: Reproduce 1-day vulnerabilities with directed differ- ential fuzzing,

Reference 38

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source=pdf_text observed=2026-08-01T05:55:24.115107Z digest=sha256:d78d0ce12946cf91e74661337413076969fbabe6b1886fad9b4e4802a8f73a1f

Observation 51671f6b-a192-47b3-8106-e2fcdee86469 · outbound

This paper cites Bullseye: Detecting prototype pollution in npm packages with proof of concept exploits,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Bullseye: Detecting prototype pollution in npm packages with proof of concept exploits,

Reference 39

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source=pdf_text observed=2026-08-01T05:55:24.190644Z digest=sha256:55727213a968b275bcd8931ac27c8f95a98622841905604a74daa54f6ae5138f

Observation 4935e060-8164-41eb-88a0-37a0705491f9 · outbound

This paper cites Secbench. js: An executable security benchmark suite for server-side javascript,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Secbench. js: An executable security benchmark suite for server-side javascript,

Reference 40

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source=pdf_text observed=2026-08-01T05:55:24.268617Z digest=sha256:201e1ea6b6625953916a87cb21bd2be3c3ead7d4df59e3219b84b75bcd2d3a63

Observation 228a1dfe-f5b0-404c-9103-c285d3ff22d8 · outbound

This paper cites Prompt engineering in llms for automated unit test generation: A large-scale study,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Prompt engineering in llms for automated unit test generation: A large-scale study,

Reference 41

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source=pdf_text observed=2026-08-01T05:55:24.330430Z digest=sha256:90fa693175504d02e4949de3b8a4d1e4911dae9dfbdaf57d0c44faae79dc9cf5

Observation ade1adc2-7217-4d4f-9852-3dbb145879a9 · outbound

This paper cites Large language model for vulnerability detection: Emerging results and future directions,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Large language model for vulnerability detection: Emerging results and future directions,

Reference 42

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source=pdf_text observed=2026-08-01T05:55:24.410645Z digest=sha256:7b992dc917534966b6a4fa65af9193ea7f254343afb397aca500530e5938c1fb

Observation a02f0d6c-de5c-4923-86a9-0c84d74ab26e · outbound

This paper cites Examining zero-shot vulnerability repair with large language models,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Examining zero-shot vulnerability repair with large language models,

Reference 43

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source=pdf_text observed=2026-08-01T05:55:24.465627Z digest=sha256:b5d6a530879a7a5654b38653916fcfe9718d407bb23ec1ed54da60e4006379c2

Observation a3c15560-a20a-48ad-b45a-0176e0de413a · outbound

This paper cites Regular expression denial of service (ReDoS) in codemir- ror,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Regular expression denial of service (ReDoS) in codemir- ror,

Reference 44

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source=pdf_text observed=2026-08-01T05:55:24.519832Z digest=sha256:863867fbf0e88b63015ed3cb123ab805e509e63112de0ebc6ffb277f13e94beb

Observation 8edcbf94-2b3a-43b7-8902-b292f9e1d9a8 · outbound

This paper cites Prototype Pollution in immer,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Prototype Pollution in immer,

Reference 45

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source=pdf_text observed=2026-08-01T05:55:24.602570Z digest=sha256:cf0e60a6aad47282ef4d4df9ed033c1a2889f9eb247329430c1b8dbb99323161

Observation e0196930-d8de-44d7-98c2-fc22ff20505d · outbound

This paper cites Just-in-time defect prediction on javascript projects: A replication study,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Just-in-time defect prediction on javascript projects: A replication study,

Reference 46

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source=pdf_text observed=2026-08-01T05:55:24.678334Z digest=sha256:bca0f41f3f3fdf1833f65d46c96102a94d214525f1068e0eec6d2d55a4cc9745

Observation be717e5f-db91-4840-8be4-5ad4dc3b222a · outbound

This paper cites Jit-smart: A multi-task learning framework for just-in-time defect prediction and localization,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Jit-smart: A multi-task learning framework for just-in-time defect prediction and localization,

Reference 47

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source=pdf_text observed=2026-08-01T05:55:24.763912Z digest=sha256:ba40332c5738cfc090fa3b6af832e78a17a8d76cd4fdc286f31c4406b9c0419b

Observation 8ec165c9-24a5-4ca3-9e22-f4f1a0cf4807 · outbound

This paper cites Bridging expert knowledge with deep learning techniques for just-in-time defect prediction,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Bridging expert knowledge with deep learning techniques for just-in-time defect prediction,

Reference 48

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source=pdf_text observed=2026-08-01T05:55:24.817873Z digest=sha256:56384799c2bbd0f972632028d390ac57e5405687315cb06be79bfe8700ec6db1

Observation 5eb3c1ad-e03d-4dc1-b248-98d989eb71a3 · outbound

This paper cites A large-scale empirical study of just-in-time quality assurance,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution A large-scale empirical study of just-in-time quality assurance,

Reference 49

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source=pdf_text observed=2026-08-01T05:55:24.874163Z digest=sha256:2057aaf8738ffeb3aad9f254bba14b11f005aee827e161394fda1a4e8eb7742e

Observation 6c42e4a9-be34-4d7e-ace3-1941a9fa30ed · outbound

This paper cites An empirical study of just-in-time defect prediction using cross-project models,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution An empirical study of just-in-time defect prediction using cross-project models,

Reference 50

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source=pdf_text observed=2026-08-01T05:55:24.952369Z digest=sha256:d942ef74805c132d11aee7d6b8950e0269e1f051e6a93ceef311797262e66de3

Observation d61e3596-85fa-4775-9c88-b2a0c2e15c00 · outbound

This paper cites Deep just-in-time defect prediction: how far are we?.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Deep just-in-time defect prediction: how far are we?

Reference 51

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source=pdf_text observed=2026-08-01T05:55:25.004959Z digest=sha256:1cb9ad419f05bfb3f4b3e26ed7c680cf46d3b61c72b7a297284cc4371fab7549

Observation 3cc13c77-49a7-48ec-bdbf-1406c8e42811 · outbound

This paper cites Cc2vec: Distributed representations of code changes,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Cc2vec: Distributed representations of code changes,

Reference 52

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source=pdf_text observed=2026-08-01T05:55:25.082949Z digest=sha256:536640fbede76ccc288a946e20a1ccd5e8ea23779c54ed44ba69c6ed894c1e49

Observation e49c54d2-96b2-4391-9631-b8a469a6fda8 · outbound

This paper cites Deepjit: an end-to-end deep learning framework for just-in-time defect prediction,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Deepjit: an end-to-end deep learning framework for just-in-time defect prediction,

Reference 53

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source=pdf_text observed=2026-08-01T05:55:25.164442Z digest=sha256:aa2b6f1372f5572fde2d8f834e5083c1731dd94d2b6ad26e77b9c892bb2542ba

Observation 08ab6cdd-8b0d-4e19-85af-ea367b7b94c9 · outbound

This paper cites Toward realistic evaluations of just-in-time vulnerability prediction,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Toward realistic evaluations of just-in-time vulnerability prediction,

Reference 54

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source=pdf_text observed=2026-08-01T05:55:25.220784Z digest=sha256:bbeaa516dc6c565d8a08068564b9405e489cbb1bb9fbbbdd12c52dc49ad3fd88

Observation 32791004-5ba9-4f0d-bb01-49d672c06f7b · outbound

This paper cites Vccfinder: Finding potential vulnerabilities in open-source projects to assist code audits,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Vccfinder: Finding potential vulnerabilities in open-source projects to assist code audits,

Reference 55

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source=pdf_text observed=2026-08-01T05:55:25.301796Z digest=sha256:dc721f90e15ae3db56a6720e4bfac259aae91beefb8e2bf91222f4758dfaa999

Observation f9b279da-29e8-48ed-8f2e-5eb9511b18bc · outbound

This paper cites Fine-grained commit-level vulnerability type prediction by cwe tree structure,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Fine-grained commit-level vulnerability type prediction by cwe tree structure,

Reference 56

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source=pdf_text observed=2026-08-01T05:55:25.386666Z digest=sha256:4eecf93483afb23847174e696fbd51542e5fed7e0d8c73825972168336a54fdb

Observation 82bbdfc9-e345-4f31-8ad6-a0f79561f1b6 · outbound

This paper cites Commit- level, neural vulnerability detection and assessment,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Commit- level, neural vulnerability detection and assessment,

Reference 57

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source=pdf_text observed=2026-08-01T05:55:25.443332Z digest=sha256:896485697d235ff5315e6fe0dbbdbfbdb777c1fdf2edd8dd57d81271539f9723

Observation 34bae5cf-b476-4157-af94-d29b1307051b · outbound

This paper cites Deepcva: automated commit-level vulnerability assessment with deep multi-task learning,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Deepcva: automated commit-level vulnerability assessment with deep multi-task learning,

Reference 58

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source=pdf_text observed=2026-08-01T05:55:25.499664Z digest=sha256:81b891128ccaea75650c35d8b1a72b23f10e8d37659088b3e1d488bff3b99b1e

Observation 03adf4a7-29c3-40a0-80db-b7e84a505478 · outbound

This paper cites Testora: Using natural language intent to detect behavioral regressions,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Testora: Using natural language intent to detect behavioral regressions,

Reference 59

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source=pdf_text observed=2026-08-01T05:55:25.582039Z digest=sha256:4237a98d1fad2203007d52861f1edeb00bd56f48560cbe1f6fc8d714428c0488

Observation b837d5d4-073c-4bed-a608-b377c12cd17c · outbound

This paper cites Pareto prompt optimization,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Pareto prompt optimization,

Reference 60

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source=pdf_text observed=2026-08-01T05:55:25.642778Z digest=sha256:1f7e1500c64cc1b7804dd028f3c93402cd9d3dc8ddc1880a9d2264f08ad45e79

Observation cff2c093-5477-4164-8fca-9589ba41d458 · outbound

This paper cites Metagpt: Meta programming for a multi-agent collaborative framework,.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Metagpt: Meta programming for a multi-agent collaborative framework,

Reference 61

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source=pdf_text observed=2026-08-01T05:55:25.648463Z digest=sha256:4a841590f5ba8627cfad869bdbce5f8ef7b53a986face1b3d63a760f1299bc47

Observation cdc04542-6869-487b-bf3f-8a1fdbd5eeea · outbound

This paper cites DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 62

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source=pdf_text observed=2026-08-01T05:55:25.655192Z digest=sha256:0cfe16e8ce4a69254c0a55d4915c69ef793ce3ed1ab8ec4c15f338d89658081f

Observation f50f5754-f44c-452b-9a70-2edbb9f94831 · outbound

This paper cites Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding.

PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

Reference 63

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source=pdf_text observed=2026-08-01T05:55:25.661951Z digest=sha256:5dad6a8c70c1b2ab60a0f51e1b144b40840a9939ff464159124ad2809385e96d

Pith citing papers

No inbound Pith citation observations are available.