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pith:LSIQ2D3N

pith:2025:LSIQ2D3NKRNT4NWJOJZV66XMVR
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LLM Harms: A Taxonomy and Discussion

Abhejay Murali, Amit Dhurandhar, David Atkinson, Junfeng Jiao, Kevin Chen, Saleh Afroogh

A taxonomy of LLM harms across five lifecycle stages supports mitigation strategies and a dynamic auditing system for responsible development.

arxiv:2512.05929 v2 · 2025-12-05 · cs.CY

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2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

It proposes mitigation strategies and future directions for specific domains and a dynamic auditing system guiding responsible development and integration of LLMs in a standardized proposal.

C2weakest assumption

That the listed categories (pre-development, direct output, misuse and malicious application, downstream application) plus an implied fifth category comprehensively capture all relevant LLM harms without significant omissions.

C3one line summary

This paper proposes a taxonomy of LLM harms in five categories and suggests mitigation strategies plus a dynamic auditing system for responsible development.

References

265 extracted · 265 resolved · 19 Pith anchors

[1] OpenAI’s ChatGPT to hit 700 million weekly users, up 4x from last year 2025
[2] ChatGPT continues to be one of the fastest-growing services ever | The Verge 2025
[3] Generative AI and jobs, 2025 · doi:10.54394/hetp0387
[4] International AI Safety Report 2025 - GOV .UK 2025
[6] LLaMA: Open and Efficient Foundation Language Models 2023 · arXiv:2302.13971

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Receipt and verification
First computed 2026-05-18T03:10:11.662747Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

5c910d0f6d545b3e36c972735f7aecac694ebc8ee729624031659cea29985e30

Aliases

arxiv: 2512.05929 · arxiv_version: 2512.05929v2 · doi: 10.48550/arxiv.2512.05929 · pith_short_12: LSIQ2D3NKRNT · pith_short_16: LSIQ2D3NKRNT4NWJ · pith_short_8: LSIQ2D3N
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LSIQ2D3NKRNT4NWJOJZV66XMVR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 5c910d0f6d545b3e36c972735f7aecac694ebc8ee729624031659cea29985e30
Canonical record JSON
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    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "primary_cat": "cs.CY",
    "submitted_at": "2025-12-05T18:12:21Z",
    "title_canon_sha256": "2cf3232237ec6493524fc3baa3b639656b199b95bdad91cb322e008107a06c59"
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