Claim LD06-SL-C27 (v1.0)
Surveying 104 papers and six industry cases, researchers found that AI red-teaming is “an unstructured procedure with undefined scope” with no unified protocols for reporting results, and argued that treating it as a cure for every risk verges on security theater.
Provenance and status
- Claim type
- Estimate and Interpretation
- Publication status
- Eligible for publication, with qualifiers: interpretation label
- Verification state
- Checked against the primary source. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
- Does not license
- A judgement on any particular red-teaming exercise
- Limitations
- Literature to 2024; the conclusion is the authors’ argument
- Source (as cited)
- Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897
- Source type
- Peer-reviewed conference paper (recorded as “Peer-reviewed conference paper”)
- Institutional position
- Independent academic
- Maturity of the evidence
- Emerging evidence
- Recorded date
- 15 October 2026
- Currentness / correction
- This is the current version. No correction or concern is recorded on its sources.
Sources this version cites
Version
This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.
This pinned version is also the current version.
Raw record: every recorded field, with its coded values
All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.
- Claim type (coded)
- ESTIMATE + INTERPRETATION (as printed: “ESTIMATE / INTERPRETATION”)
- Publication status (coded)
- ELIGIBLE (INTERPRETATION_LABEL) (as printed: “ELIGIBLE WITH INTERPRETATION LABEL”)
- Verification state (coded)
- CONTENT_VERIFIED_PRIMARY
- Source type (coded)
- PEER_REVIEWED_CONFERENCE_PAPER
- Maturity (coded)
- EMERGING
| # | Canonical field | Canonical value (verbatim) | Normalized (Claim Value Vocabulary v1.0.2) |
|---|---|---|---|
| 1 | CLAIM ID | LD06-SL-C27 | — |
| 2 | PUBLIC-FACING CLAIM | Surveying 104 papers and six industry cases, researchers found that AI red-teaming is “an unstructured procedure with undefined scope” with no unified protocols for reporting results, and argued that treating it as a cure for every risk verges on security theater. | — |
| 3 | CLAIM TYPE | ESTIMATE / INTERPRETATION | base_types: ESTIMATE, INTERPRETATION |
| 4 | RESEARCH STRAND | C — Evaluation in governance, audits and red-teaming | — |
| 5 | MATURITY | EMERGING | states: EMERGING |
| 6 | SOURCE | Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897 | source_ids: SRC-SL-FEFFER-ET-AL-RED-TEAMING-AIES-2024 |
| 7 | SOURCE TYPE | Peer-reviewed conference paper | classes: PEER_REVIEWED_CONFERENCE_PAPER |
| 8 | INSTITUTIONAL POSITION | Independent academic | — |
| 9 | SCOPE | Red-teaming of generative AI, to 2024 | — |
| 10 | DESIGN | Literature survey and case studies | — |
| 11 | SAMPLE / DATASET | 104 papers; six industry case studies | — |
| 12 | MODEL / TOOL / VERSION | Generative AI systems in the surveyed work | — |
| 13 | COMPARATOR | None | — |
| 14 | MAIN RESULT | 69 of 104 papers concern dissentive risks; 42 use brute-force plus AI methods; no unified reporting protocols; developer responses to published jailbreaks muted and mixed | — |
| 15 | INFERENTIAL TARGET | Whether red-teaming is a reliable evaluation method | — |
| 16 | DOES NOT LICENSE | A judgement on any particular red-teaming exercise | — |
| 17 | VERIFICATION STATE | CONTENT VERIFIED | state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN |
| 18 | LIMITATIONS | Literature to 2024; the conclusion is the authors’ argument | — |
| 19 | CONTRADICTORY / SUPPORTING SOURCES | Practitioners also separate red-teaming from measurement | — |
| 20 | GEOGRAPHY / LANGUAGE | Not checked / English | — |
| 21 | DISTRIBUTIONAL LEDGER | Not analysed in this claim | — |
| 22 | R01 / FIELD-MAP LINKS | LD-06, D4b | — |
| 23 | WHAT WOULD CHANGE THIS CLAIM? | Standard red-teaming protocols with reported coverage | — |
| 24 | PUBLICATION STATUS | ELIGIBLE WITH INTERPRETATION LABEL | state: ELIGIBLE · qualifiers: INTERPRETATION_LABEL |
| 25 | RECORDED DATE | 2026-10-15 | — |
Field 22 references resolved against Field Map v0.2: LD-06 (LIVING_DEBATE); D4b (SUB_AREA)
The sources as recorded, with their record IDs and coded source types:
Pinned Sources (field 6, version-specific)
- “Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897” → Feffer et al. (2024), “Red-Teaming for Generative AI…” (SRC-SL-FEFFER-ET-AL-RED-TEAMING-AIES-2024--v1.0) — Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897; class PEER_REVIEWED_CONFERENCE_PAPER (explicit public provenance pin)
Cited by Living Debate versions
Cite this page
SocioLens project. (2026, October 15). Surveying 104 papers and six industry cases, researchers found that AI red-teaming is “an unstructured procedure with undefined scope” with no unified protocols for reporting results, and argued that treating it as a cure for every risk verges on security theater [Claim LD06-SL-C27, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld06-sl-c27/v1.0/
@misc{sociolens_ld06_sl_c27_v1_0,
author = {{SocioLens project}},
title = {Surveying 104 papers and six industry cases, researchers found that AI red-teaming is “an unstructured procedure with undefined scope” with no unified protocols for reporting results, and argued that treating it as a cure for every risk verges on security theater.},
year = {2026},
date = {2026-10-15},
howpublished = {\url{https://sociolens-web.pages.dev/claims/ld06-sl-c27/v1.0/}},
note = {Claim LD06-SL-C27, version v1.0}
}The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.
Research-of-record provenance
Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 9042aeba88c1debe44e629722295b2b06ef0962fa865cb99c95e0e13eae726ef.
Repository-authored inputs (not Drive exports)
- SocioLens claim — LD06-SL-C27 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-06 debate; a new Claim, first version, written from the project's LD-06 source notes, checked against full texts or official abstracts. Block “ENTRY 27 — LD06-SL-C27” SHA-256
8d186b2ee062e2f944aac28451ce77704b7e4ebceeb6091e0835286bc650174b; fixturefixtures/authored/claims/LD06-SL-C27--v1.0.
Builder decisions (not Drive data)
- VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD06-SL-C27 v1.0); the first version of a new Claim of the LD-06 debate
- CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD06-SL-C27 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-06 debate; a new Claim, first version, written from the project's LD-06 source notes, checked against full texts or official abstracts
- FIELD_6_SOURCE_REFERENCE = SRC-SL-FEFFER-ET-AL-RED-TEAMING-AIES-2024--v1.0 — basis: field 6 of ENTRY 27 — LD06-SL-C27 cites "Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
- SOURCE_PROVENANCE_PIN = SRC-SL-FEFFER-ET-AL-RED-TEAMING-AIES-2024--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Feffer, Sinha, Deng, Lipton & Heidari, “Red-Teaming for Generative AI: Silver Bullet or Security Theater?,” AIES 2024, arXiv:2401.15897".