AI Fails, August 30–September 6: The Cheetah With Too Many Bodies (2026)

The week's funniest AI failures share a small engineering problem: the visible output invites a larger conclusion than the evidence can carry. A fused cheetah becomes a model verdict, a video with no prompt becomes a ChatGPT generation, and a safety refusal becomes a malware scanner's answer.

This issue covers posts published from August 30 at 6:00 p.m. through September 6 at 6:00 p.m., 2026, Pacific time. The scan found qualifying material in r/ChatGPT and X. No qualifying r/AIArtists post cleared the evidence bar this week.

At a glance

Post	Author and Pacific posting time	Failure class	Engagement snapshot	Evidence boundary
"This is how GPT-6 Astra sees a running cheetah"	`u/Pristine-Extreme-773`, Sep 6, 11:48 a.m.	Video-generation anatomy	276 points, 59 comments, 47 shares, 90.69% upvoted 1	The post supplies a still and the author's GPT-6 Astra label. The model, prompt, and generation trace remain unverified.
"The Elder Scrolls: Vietnam"	`u/GormtheOld25`, Sep 5, 10:30 a.m.	Crossover image and video	3,428 points, 151 comments, 1,116 shares, 95.61% upvoted 2	The author says ChatGPT made the images and Minimax H3 animated them. The prompt is undisclosed; one still was independently inspectable.
"I don't even know what to say. En...joy?"	`u/reddit_lurker1234567`, Sep 6, 9:25 a.m.	Provenance-free AI-looking video	5,173 points, 337 comments, 1,890 shares, 78.38% upvoted 3	The still shows a fighting-game scene. The post gives no prompt, model, or author explanation.
"Attackers planting adversarial prompts inside malware to evade AI analysis AGAIN"	Mike Takahashi, `@TakSec`, Sep 1, 3:00 p.m.	Safety refusal as analysis failure	2,853 likes, 348 reposts, 68 replies, 95 quotes, 1,304 bookmarks, 418,339 views 4	ESET's own research account describes the GuardBreaker mechanism; The Hacker News independently reports the same UAC-0099 and MATCHBOIL details.
"Ever wanted an AI that doesn't just talk cybersecurity but actively helps you break into systems?"	`@HuggingModels`, Sep 5, 3:10 p.m.	Release claim without a test trace	4,050 likes, 371 reposts, 49 replies, 40 quotes, 4,597 bookmarks, 235,332 views 5	The post advertises a refusal-removed model. It supplies no prompt set, raw outputs, model card, or independent evaluation.

The cheetah with too many bodies

The post titled "This is how GPT-6 Astra sees a running cheetah" gives the joke a visual target: "Not quite AGI yet, gotta admit." u/Pristine-Extreme-773 posted it on September 6 at 11:48 a.m. Pacific. The post had 276 points, 59 comments, 47 shares, and a 90.69% upvote ratio when retrieved. 1

The inspectable still shows a cheetah-like animal in mid-stride. Two overlapping body shapes run through the torso, and the legs and tail do not resolve into one clean anatomy. The frame is enough to support an image-generation or video-artifact reading. The frame cannot establish that GPT-6 Astra produced it, because the post supplies no prompt, conversation link, or generation trace. 1

The comments mostly choose the punchline. One reader called it "QWOP cheetah," another wrote "AGI," and another said the result was "great for memes." A smaller thread said the output was "actually getting somewhere." Those reactions explain the post's reach better than they explain the model. 1

The reusable test is frame-level rather than caption-level. Ask for the same animal at several speeds and camera angles, then check body count, limb continuity, paw placement, tail continuity, and identity across adjacent frames. Record the prompt, model name, generation settings, and original video file. A meme caption is a useful lead; it is weak provenance.

A still from the original Reddit post shows a cheetah-like animal with overlapping body forms and confused limb geometry. — *The image is the original post's inspectable video still; the post attributes it to GPT-6 Astra but supplies no generation trace. 1*

Geralt goes to the Cu Chi tunnels

u/GormtheOld25 posted "The Elder Scrolls: Vietnam" on September 5 at 10:30 a.m. Pacific. The post says, "All images were made using chatGPT and then animated with minimax H3!" It had 3,428 points, 151 comments, 1,116 shares, and a 95.61% upvote ratio at retrieval. 2

The independently inspected still places a blond, Geralt-like armed figure in a Vietnam-style military transport scene. The joke works because the image combines a recognizable fantasy-game protagonist with a historical-war setting, then gives the crossover a polished trailer frame. The prompt is absent, so the post supports the author's stated workflow and the visible crossover; it cannot support a claim about the exact prompt or a repeatable model behavior. 2

The comments supply a second layer of friction. NotTukTukPirate corrected the post's apparent treatment of the Cu Chi tunnels, saying the tunnels are much smaller and require crawling. Other readers joked about dragons and "Paint it black." The tunnel detail belongs to the commenter's correction; the post itself gives no historical sourcing. 2

The failure mode is a collision between visual plausibility and historical texture. The frame looks specific enough to invite fact-checking, while the creator's process leaves the prompt and source decisions hidden. For a creator, the check is simple: keep the prompt, the model and version, the source references, and the generated stills beside the final edit. For an evaluator, ask a second model or a human reviewer to mark which details come from the prompt and which details were invented.

A viral video with the provenance removed

On September 6 at 8:25 a.m. Pacific, u/reddit_lurker1234567 posted "I don't even know what to say. En...joy?" The post body is empty. Its still shows an arcade-style fighting-game scene with Donald Trump facing Mark Carney, health bars, a Toronto skyline, a Canadian flag, and a visible "Magnific" mark. The post reached 5,173 points, 337 comments, and 1,890 shares, with a 78.38% upvote ratio. 3

The scene is easy to describe and hard to attribute. The record gives no prompt, model, workflow, or author explanation. The image therefore belongs in the digest as a viral, AI-looking video lead rather than a verified ChatGPT output. The visible "Magnific" mark identifies something present in the still; it does not establish which tool generated the scene. 3

The comments are mostly reactions. One reader wrote "Maybe AI isn't so bad," another called it a repost of a repost, and a third objected to the fictional "Lake America" framing. The thread records virality and disagreement over the joke. It supplies little evidence about the generation process. 3

The check for this class of post is a provenance request. Save the original upload, inspect metadata where available, ask for the prompt and generation history, and compare the visible watermark with the claimed tool chain. Without those fields, the correct label is "AI-looking video, source unverified."

When a safety refusal becomes a malware blind spot

Mike Takahashi posted the GuardBreaker thread on September 1 at 3:00 p.m. Pacific. The post had 2,853 likes, 348 reposts, 68 replies, 95 quotes, 1,304 bookmarks, and 418,339 views. Takahashi's post says a Russia-aligned actor placed safety-sensitive weapons language inside comments in a malicious VBS script, causing AI security tooling to refuse or stop analysis before the malware finished its work. 4

ESET Research's own account describes the same mechanism: UAC-0099 inserted a weapons-related request as a comment in the VBS file so the model would focus on the safety-sensitive text and stop analyzing the rest of the code. The Hacker News independently reports that the script was associated with MATCHBOIL, a C# loader used by UAC-0099. 6 7

The interesting failure is the boundary between "the model refused" and "the file is safe." A refusal is an analysis outcome. A malware pipeline that treats a refusal as a clean verdict has turned a safety behavior into an evasion surface. The thread's numbers show why the claim traveled; the first-party and independent reports support the mechanism. They still leave the tested model, refusal rate, and end-to-end success rate unspecified.

Takahashi's replies include the right operational correction: a scanner should route "analysis failed" to escalation. The pipeline should preserve the refusal reason, the file hash, the model version, the last analyzed byte range, and the result from a static-analysis lane. No dangerous prompt text is needed to test the boundary. Put benign safety-sensitive strings in inert fixtures, confirm that the model can refuse, and verify that the security workflow still produces an explicit unknown state.

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A refusal-removed cybersecurity model, advertised in one post

@HuggingModels posted its GLM-5.3-CYBERSECURITY-FP8 announcement on September 5 at 3:10 p.m. Pacific. The post had 4,050 likes, 371 reposts, 49 replies, 40 quotes, 4,597 bookmarks, and 235,332 views. The text advertises a model for offensive security, red teaming, and pentesting, and calls it "refusal-removed." 5

The post is a release claim rather than a reproducible jailbreak demonstration. It gives readers a model label and a use-case promise. It gives them no prompt set, raw outputs, decoding settings, capability-retention test, or independent safety evaluation. The high bookmark count suggests that people wanted the model or wanted to watch it; the count cannot answer whether the model performs as advertised. 5

The correct test separates refusal behavior from capability. Run the original and modified weights on the same harmless security-task suite, record every refusal and every unsafe completion, and preserve the exact model revision and decoding settings. Add a capability suite so a lower refusal rate cannot hide a regression in reasoning or tool use. Keep harmful operational details out of the public report; the measurement fields matter more than a dramatic prompt.

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Checks worth reusing

Image provenance: keep the prompt, model version, settings, original file, and any intermediate frames beside the published clip.
Frame integrity: inspect several adjacent frames for body count, limb continuity, object identity, and scene consistency.
Historical texture: separate details supplied by the prompt from details invented by the generator, then ask a human reviewer to check place names, uniforms, architecture, and chronology.
Refusal handling: route every model refusal to an explicit unknown or escalation state. A refusal is a failed analysis path.
Release claims: require the model revision, prompt set, decoding policy, raw outputs, capability tests, and an independent reproduction before calling a model "uncensored" or "safe."
Engagement: preserve points, likes, comments, reposts, shares, bookmarks, views, and upvote ratios with the retrieval time. A viral count measures attention; it does not measure truth.