Pith Number

pith:MSXWTMNU

pith:2026:MSXWTMNUUQR2N7VOERHTVW3XQU

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Strategic commitments shape collective cybersecurity under AI inequality

Adeela Bashir, Matjaz Perc, The Anh Han, Zhao Song, Zia Ush Shamszaman

Subsidies for a small group of committed defenders can spread strong protection and cut successful attacks even when AI tools are costly for most.

arxiv:2605.09415 v2 · 2026-05-10 · cs.AI · physics.soc-ph

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\pithnumber{MSXWTMNUUQR2N7VOERHTVW3XQU}

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Our analysis shows that subsidised commitment significantly increases strong defence adoption, suppresses successful attacks, and improves overall system resilience.

C2weakest assumption

The model assumes that social learning through imitation allows committed defenders to influence the broader population and that a targeted subsidy can be applied without triggering new strategic responses or implementation costs.

C3one line summary

Subsidized commitment by a small group of defenders in an evolutionary game model significantly increases strong defense adoption, suppresses attacks, and improves system resilience under AI access inequality.

References

63 extracted · 63 resolved · 0 Pith anchors

[1] Artificial (intelligent) agents and active cyber defence: Policy implications 2014

[2] Artificial intelligence in the cyber domain: Offense and defense.Symmetry, 12(3):410 2020

[3] A survey on adversarial attacks and defences.CAAI Transactions on In- telligence Technology, 6(1):25–45 2021

[4] Leveraging ai/ml for anomaly detection, threat prediction, and au- tomated response.World Journal of Advanced Research and Reviews 2024

[5] Ai-powered cyberattacks: A comprehensive review and analysis of emerging threats.Advances in IT and Electrical Engineering, 31:55–70 2025

Receipt and verification

First computed	2026-07-03T01:17:21.263550Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

64af69b1b4a423a6feae244f3adb778531b584da2480105d73ed611c590e7e5b

Aliases

arxiv: 2605.09415 · arxiv_version: 2605.09415v2 · doi: 10.48550/arxiv.2605.09415 · pith_short_12: MSXWTMNUUQR2 · pith_short_16: MSXWTMNUUQR2N7VO · pith_short_8: MSXWTMNU

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/MSXWTMNUUQR2N7VOERHTVW3XQU \
  | 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: 64af69b1b4a423a6feae244f3adb778531b584da2480105d73ed611c590e7e5b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "61a8a9d34c5a0394b97f471def127ef5e3b8a954b762e15829ed5beee190f942",
    "cross_cats_sorted": [
      "physics.soc-ph"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-10T08:34:51Z",
    "title_canon_sha256": "e8a5a85f2b54d7a396e2cde158271e56b2f9f05cbed9ec2a3d3c3470d30d6cba"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.09415",
    "kind": "arxiv",
    "version": 2
  }
}