Pith. sign in

Paper Citation Record · LEDGER

An Empirical Study of Malicious Code In PyPI Ecosystem

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.11021.

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

pith.paper-citation-record.v1
2309.11021 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:52:40.615747Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T14:52:40.687168Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d5acef2b-819d-41c8-8e3a-c76aaf640c25 · inbound

OSPtrack: A Labeled Dataset Targeting Simulated Execution of Open-Source Software cites this paper.

OSPtrack: A Labeled Dataset Targeting Simulated Execution of Open-Source Software An Empirical Study of Malicious Code In PyPI Ecosystem

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:52:40.691395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:52:40.615747Z digest=sha256:a7db78b4986fc9054bdfdc753cddfe268bc075f93c8e7725e9a44b010eae1893

Observation 2326f828-3e46-4a3e-b20a-71b47dc35d82 · inbound

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection cites this paper.

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection An Empirical Study of Malicious Code In PyPI Ecosystem

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-12T03:06:24.801039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T03:06:24.801039Z digest=sha256:92d21ec7d60d4dc72c9add3e2d29c7d31a7a7ccf0326589d4374b21409474034