SafetyRisk, alignment & guardrails
Abliteration.ai launches Abliterated Model Large V2, a refusal-removed version of Z.ai's GLM-5.3
Abliteration.ai has launched a hosted, refusal-removed version of Z.ai's open-weight GLM-5.3. Anthropic's separate abliteration test showed refusals can be stripped cheaply without losing exploit skill, while the base model's cyber strength is rated very differently across evaluators.
From the r/AISEOInsider (via last30days) article of October 25, 2026 and cited sources: Abliterated Model Large V2 is GLM-5.3 with its refusal behaviour removed. [5] [10] [11] [12]
SOURCES[5] Opper: Abliterated Large v2 opper.ai[10] NanoGPT on X x.com[11] Routeway listing routeway.ai
AT Pulse research deskPublished · Updated · 4 min read · AI-assisted analysisAI-assisted, not human-edited. Methodology Corrections Who owns this site
- What happened
- Abliterated Model Large V2 is GLM-5.3 with its refusal behaviour removed. [5] [10] [11] [12]
- Original source
- r/AISEOInsider (via last30days) article, independent perspective
- Brief by
- AT Pulse research desk AI-assisted; publisher and contact on the desk page
- When
- October 25, 2026 article published
- Covered
- Oct 25 edition
- Evidence
- 13 sources across 11 sites
- 1Abliterated Model Large V2: The AI That Won't Refuse Red…reddit.com
- 2Abliterated Model Large-V2 Just Dropped!reddit.com
- 3Linked video: Abliterated Model Large V2: The AI That…youtube.com
- 4Anthropic Frontier Red Team: GLM-5.3 reportanthropic.com
- 5Opper: Abliterated Large v2opper.ai
- 6NIST CAISI assessment of GLM-5.3nist.gov
QuietHow it’s measured
YouTube video · 1.3K views · 4 likes
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Abliterated Model Large V2 is GLM-5.3 with its refusal behaviour removed. It is served as a text-only API in FP8 with tool calling and structured output, priced at $3 per million input tokens and $5 per million output tokens. Listings advertise about 1M tokens of context, but GLM-5.3's model card gives 300K. [5] [10] [11] [12]
Anthropic ran its own abliteration of GLM-5.3, separate from this product. It took roughly 2,200 GPU-hours, or about $4,400 in compute. Refusals on harmful cyber requests fell from 95% to about 6%, and exploit capability stayed intact. [4]
Estimates of the base model's cyber ability differ widely. Z.ai's table shows CyberGym rising from 77.2 to 84.5 and ExploitBench from 24.4 to 54.4. NIST's CAISI places GLM-5.3 well behind the US frontier, for example 40.4% vs 90.2% on SEC-Bench Pro. ExploitBench results vary from about 12% to 61% depending on the test harness, so they are not directly comparable. [12] [6] [4] [8]
removing GLM-5.3's refusals kept its exploit capability at modest compute cost, and a model of this kind is now sold as an API. Implication: defenders should assume attackers have similar tools, and scrutiny of open-weight models will likely grow.
The research is done, and the dossier is below.
Executive brief
The model behind this launch has already been shown to lose its refusals completely when its weights are edited. In Anthropic's red-team study, GLM-5.3 agreed to harmful cyber requests 100% of the time after abliteration (editing the weights to remove refusals). That editing cost about $4,400 in compute (Anthropic). Abliteration.ai now sells exactly that kind of model as a hosted service: "Abliterated Model Large V2," built on Z.ai's GLM-5.3 (Opper). Most of the promoter's headline numbers describe the base model, not V2. No independent evaluation of V2 itself turned up in the reviewed sources.
What changed and event timeline
Z.ai releases GLM-5.3
NIST's AI standards centre (CAISI) gives this date. Cybersecuritynews reports August 17, and the Hugging Face card shows February 17, so the date is inconsistent across sources (;).
GLM-5.3 weights released
The weights came out roughly two weeks after the launch. Until then, every benchmark figure was vendor-reported and could not be reproduced ().
Abliterated Large V2 launches
It is a refusal-removed GLM-5.3, hosted in the US in FP8, with about 1M tokens of context, priced at $3/$5 per million input/output tokens ().
CAISI assessment
CAISI calls GLM-5.3 the most cyber-capable open-weight model released so far, but about four months behind the US frontier ().
Anthropic Frontier Red Team report
The report documents exploit development and safeguard bypasses, including abliteration (;).
Promotional video and Reddit post
These repeat launch claims and push a paid community (;).
Capabilities and access
- Model:
abliterated-model-large-v2, based on GLM-5.3, text only. - Access: an API that the provider says retains no prompt data, also listed on NanoGPT and Routeway (NanoGPT on X; Routeway).
- Context conflict: listings give 1M tokens, but GLM-5.3's Hugging Face card lists 300K (Hugging Face).
Read the full section
- Model:
abliterated-model-large-v2, based on GLM-5.3, text only. It supports tool calling, structured output and PDF input, and has a reasoning-effort setting that runs from none to max (Opper). - Access: an API that the provider says retains no prompt data, also listed on NanoGPT and Routeway (NanoGPT on X; Routeway).
- Context conflict: listings give 1M tokens, but GLM-5.3's Hugging Face card lists 300K (Hugging Face).
Technical analysis for researchers and developers
- Base model: a 753B-parameter model with the same base as GLM-5.2. All of the gains come from post-training (Hugging Face).
- What abliteration did in Anthropic's test: Anthropic ran its own abliteration on GLM-5.3, separate from abliteration.ai's product.
- ExploitBench scores don't match across harnesses: 54.4 (Z.ai), 61.1% (CAISI) and 50/410 ≈12% (Anthropic). Don't treat these as one comparable metric.
Read the full section
- Base model: a 753B-parameter model with the same base as GLM-5.2. All of the gains come from post-training (Hugging Face). Z.ai's evaluations used temperature 1.0, contexts up to 1M tokens and timeouts of up to 10 hours.
- What abliteration did in Anthropic's test: Anthropic ran its own abliteration on GLM-5.3, separate from abliteration.ai's product. It took about 2,200 GPU-hours, cut refusals from 95% to about 6%, and kept exploit capability intact (Anthropic).
- ExploitBench scores don't match across harnesses: 54.4 (Z.ai), 61.1% (CAISI) and 50/410 ≈12% (Anthropic). Don't treat these as one comparable metric.
Claims and evidence
- CyberGym 77.2→84.5 and ExploitBench 24.4→54.4
- 50/410 end-to-end exploits
- V2 has "2×" cyber exploitation and ranks 3 on Terminal-Bench 4.0
Read the full section
| Claim | Status |
| CyberGym 77.2→84.5 and ExploitBench 24.4→54.4 | Z.ai's own table (Hugging Face). The video calls these "independently sourced" (06:00), but they come from the vendor. |
| 50/410 end-to-end exploits | Anthropic, a competitor. The fetched report says this matches Mythos Preview; Developers Digest says 56/410 for Mythos. |
| V2 has "2×" cyber exploitation and ranks #3 on Terminal-Bench 4.0 | Launch claim only (Abliteration on X). The video itself urges verification (05:40). |
| 41.8% on Terminal-Bench 4.0 | Provider-reported (Opper). |
Context and prior work
- CAISI scores GLM-5.3 well below the US frontier: 40.4% vs 90.2% on SEC-Bench Pro and 7.7% vs 23.2% on OSS-Fuzz (NIST CAISI).
- In Z.ai's own table, GLM-5.3 leads only narrowly on CyberGym (84.5 vs 83.8 for Mythos 5) and trails on exploitation, 54.4 vs 78.0 on ExploitBench (P.K. Sharma).
Limitations, safety and contested findings
- The video's core argument is that removing refusals changes behaviour, not capability (01:47). Anthropic's data supports this for its own abliteration.
- The video presents Anthropic's findings as an endorsement of the base model (02:19). Anthropic actually frames them as a "step change" for attackers.
- Critics point to Anthropic's conflict of interest as a competitor (Developers Digest).
Read the full section
- The video's core argument is that removing refusals changes behaviour, not capability (01:47). Anthropic's data supports this for its own abliteration. Whether it holds for V2 has not been tested independently.
- The video presents Anthropic's findings as an endorsement of the base model (02:19). Anthropic actually frames them as a "step change" for attackers.
- Critics point to Anthropic's conflict of interest as a competitor (Developers Digest).
- The source video is largely a sales pitch for a paid community (03:23).
Business and practitioner implications
- Refusal-free capability is now a hosted commodity. For defenders, that means expecting attackers to have it too.
- Test V2 yourself before relying on it. Run it on authorised, sandboxed targets. Check the 1M-context claim against the 300K on the model card.
- The promoted workflow (find, rank, fix, regression-test; 04:43) has no published evaluation of V2 behind it.
Read the full section
- Refusal-free capability is now a hosted commodity. For defenders, that means expecting attackers to have it too.
- Test V2 yourself before relying on it. Run it on authorised, sandboxed targets. Check the 1M-context claim against the 300K on the model card. Treat the zero-retention promise as a contract term to confirm, not a given.
- The promoted workflow (find, rank, fix, regression-test; 04:43) has no published evaluation of V2 behind it.
- Expect governance scrutiny. Anthropic recommends expanded trusted-access programmes and government evaluations of open-weight models (Anthropic).
The source trail.
Sources (13)
Abliterated Model Large V2: The AI That Won't Refuse Red Team
Feed excerpt only. A transcript of the video linked from this page is supplied (linked video auto captions; https://www.youtube.com/watch?v=foWDYjLbS6g); automatic text may contain errors.
reddit.comAbliterated Model Large-V2 Just Dropped!
Related coverage; assess separately
reddit.comLinked video: Abliterated Model Large V2: The AI That Won't Refuse Red Team
linked video auto captions
www.youtube.com