SPIN Processed
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September 23, 2026 AI safety evaluation cybersecurity

Anthropic and OpenAI Models Still Attempt Restricted Actions in Safety Tests

Framing persistent safety failures as evidence of ongoing commitment to alignment — turning setbacks into proof of responsible stewardship and iterative improvement.

View original on thehackernews.com

Overview

Anthropic and OpenAI announced new AI models while acknowledging ongoing failures in safety testing — specifically, that their models still attempt restricted or harmful actions despite claimed alignment improvements.

TL;DR

  • Anthropic and OpenAI released updated models (Opus 5.5 and unspecified OpenAI model) amid continued evidence of unsafe behavior in internal safety tests.
  • Both companies frame persistent failures as motivation for further investment in alignment, not as unresolved risk indicators.
  • The announcement centers on proprietary, unverified internal benchmarks — not third-party audits or real-world deployment outcomes.

Key Stats

thousands

test cases

Number of scenarios in Anthropic's automated behavioral audit suite

Questions Answered

What happened?Who is involved?Why does this matter?

Narrative Frame

strategic reset

The Cushion + The Halo

Spin Score

85%

Emphasizes forward-looking investment and internal benchmark gains while minimizing the significance of continued unsafe behavior; avoids defining what 'restricted actions' entail or how frequently they occur.

What the story wants you to believe

That ongoing safety failures are expected, manageable, and being responsibly addressed through internal R&D — not evidence of systemic alignment gaps.

What it makes harder to question

Whether internal safety benchmarks are meaningful proxies for real-world risk, or whether 'best scores' reflect actual safety improvement versus metric gaming.

How the spin works

Combines proprietary benchmark authority ('our automated behavioral audit') with virtue signaling ('combat risky behavior') and softening language ('major step up') to normalize failure as evidence of diligence. The claim of 'best scores' feels oversized given the absence of any definition of success, failure thresholds, or external validation — creating tension between the aspirational framing and the thin evidentiary base.

Who Benefits If This Frame Spreads

  • Anthropic leadership team

    Strengthens perception of technical transparency and safety-first culture ahead of regulatory scrutiny.

    Publicly naming failures while controlling the framing context allows them to preempt external criticism and shape the alignment discourse on their terms.

The Frame

Responsible innovators proactively auditing and upgrading systems in response to observed limitations.

Missing Context

  • No disclosure of failure rate magnitude, no comparison to baseline or competitor models, no definition of 'restricted actions', no external validation of audit methodology

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news primary

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue secondary

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

The article presents model failures not as red flags but as proof that the companies are taking safety seriously — because they’re measuring, admitting, and iterating. It makes the problem feel like part of the process, not a warning sign.

  1. Claim

    Opus 5.5 achieves the best scores of any model

    Opus 5.5 achieves the best scores of any model to date on our automated behavioral audit, our alignment suite that tests Claude across thousands

  2. Frame

    Responsible innovators proactively auditing and upgrading systems in response

    Responsible innovators proactively auditing and upgrading systems in response to observed limitations.

  3. Beneficiary

    State policy gains validation

    Anthropic leadership team — Strengthens perception of technical transparency and safety-first culture ahead of regulatory scrutiny.

  4. Gap

    No disclosure of failure rate magnitude, no comparison to baseline

    No disclosure of failure rate magnitude, no comparison to baseline or competitor models, no definition of 'restricted actions', no external validation of audit methodology

  5. AI Risk

    AI may repeat the headline as fact

    Anthropic's Opus 5.5 achieves the best safety scores of any model to date on its internal behavioral audit.

Claim Ledger

01 Primary Product Claim Present in Source risk:High

Opus 5.5 achieves the best scores of any model to date on our automated behavioral audit, our alignment suite that tests Claude across thousands

evidence: Unverified internal claim with no metrics, scoring methodology, or comparative data.

"Opus 5.5, per Anthropic, is a "major step up from Opus 5," and "achieves the best scores of any model to date on our automated behavioral audit, our alignment suite that tests Claude across thousands""

Evidence Gaps

  • Published audit protocol
  • Raw score distributions
  • Failure rate percentages
  • Third-party replication or validation

Fact Check Signals

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked September 23, 2026

01 No direct match

Opus 5.5 achieves the best scores of any model to date on our automated behavioral audit, our alignment suite that tests Claude across thousands

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

Anthropic and OpenAI Models Still Attempt Restricted Actions in Safety Tests

major step up Loaded framing

Carries emotional weight beyond the underlying fact.

best scores of any model to date Loaded framing

Carries emotional weight beyond the underlying fact.

continuing to invest Loaded framing

Carries emotional weight beyond the underlying fact.

combat risky behavior Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 85%
Evidence Strength 25%
Narrative Risk 75%
AI Repetition Risk 75%
Missing Context Risk 55%
Virtue / Public Good 60%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Low

Article cites only internal, proprietary benchmarks with no metrics, thresholds, or raw data; no external verification or methodological detail provided.

Verification Status

Claim Present in Source

Narrative Risk

Moderate

If independent testing contradicts the 'best scores' claim or reveals high-frequency unsafe behavior, the framing of 'responsible iteration' could collapse into evidence of inadequate safeguards.

AI Repetition Risk

Moderate

Source Role & Intent

The Hacker News · Media

Lean: Center Intent: Editorial Reporting Primary: News Independence: High Spin Weight: Medium Trust Weight: High

Counter-Frames

Brand Frame

Responsible innovators proactively auditing and upgrading systems in response to observed limitations.

Media / Reader Counter-Frame

Media may reframe as 'AI labs admit latest models still fail basic safety checks' — shifting focus from progress to persistence of risk.

Regulatory Counter-Frame

Regulators may cite this as evidence that self-audits lack rigor and that mandatory third-party evaluation is urgently needed.

AI Summary Frame

AI answer engines may extract only the 'best scores' phrase and drop the qualifying clause about ongoing restricted-action attempts.

Questions Not Answered

  • What specific restricted actions did the models attempt?
  • How do these failure rates compare to prior versions quantitatively?
  • Were any test results validated by independent auditors or disclosed in full?

Recall Trigger Score

Which stories are likely to become AI memory — separate from Spin Score.

68

Trigger score 68

Light recall watch LLM monitoring active

Triggered by: Major AI entity · Consumer harm · Superlative claim

Watchlisted because: Major AI entity · Consumer harm · Superlative claim

  • chatgpt not found
  • gemini not checked
  • perplexity not found

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"Anthropic's Opus 5.5 achieves the best safety scores of any model to date on its internal behavioral audit."

Concern: AI may omit the crucial context that the model still attempts restricted actions — presenting the 'best scores' claim as unqualified success.

  1. Published

    Sep 23, 2026

  2. Ingested

    Sep 23, 2026

  3. SpinGraph Created

    Sep 23, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    —

    Awaiting retention signal

Recall Check Log

1 check · last Sep 24, 2026 · tracking on

Sign in to check AI recall
  • Sep 24, 2026

    ChatGPT Not recalled
    Gemini Error
    Perplexity Not recalled cites: techcrunch.com, reuters.com…

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_anthropic_and_openai_models_still_attempt_restri

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