This weekend’s watchlist covers three different genres of movies, so you can pick whatever you are in the mood for. We have a trio of hidden gems on Amazon Prime Video that deserve way more attention.

There is a gritty Michael Caine revenge thriller you should not miss, a micro-budget 1950s sci-fi mystery that thrives on atmosphere and dialogue. For horror fans, we have a psychological horror bout a hospice nurse whose faith tips into something far more dangerous that gets inside your skin.

We also have guides to the best new movies to stream, the best movies on Netflix, the best movies on Hulu, the best free movies, and the best movies on Amazon Prime Video.

Saint Maud (2019)

Saint Maud is not a horror film in the traditional sense, and going in expecting one will work against you. What it actually is is a deeply unsettling psychological portrait of a young hospice nurse named Maud, a recent Catholic convert who becomes dangerously fixated on saving her terminally ill patient’s soul in ways that grow increasingly disturbing.

Morfydd Clark’s performance is the engine of the whole thing, holding a fragile, frightening line between piety and paranoia throughout. I really like how the film gets under your skin without ever fully explaining itself. You finish it feeling like you witnessed something you were not supposed to see, and that feeling does not leave quickly.

You can watch Saint Maud on Amazon Prime Video

Harry Brown (2009)

If you have a soft spot for slow-burn British crime dramas, Harry Brown is the movie you need to watch this weekend. Michael Caine plays the title character, a widowed, retired Royal Marines veteran living on a decaying South London housing estate overrun by gang violence. When his only friend is murdered, Harry stops looking the other way.

What makes this film work so well is how it refuses to glamorize what follows. Harry is not an action hero. He is an old man with emphysema who stumbles during a chase and collapses on a canal path.

I really like how the film earns every moment of tension because it keeps Harry vulnerable and the world around him genuinely threatening. Caine is absolutely extraordinary here, and there are sequences in this film that will make you forget you are watching a 77-year-old man.

You can watch Harry Brown on Amazon Prime Video

The Vast of Night (2019)

Have you accidentally tuned into a late-night radio broadcast and could not bring yourself to switch off. Well, The Vast of Night is exactly that kind of sci-fi movie.

Set over a single night in 1950s small-town New Mexico, the film follows Fay, a teenage switchboard operator, and Everett, a fast-talking local radio DJ, as they stumble onto a mysterious audio frequency that sends them down a strange and increasingly eerie rabbit hole.

There are no big set pieces or alien invasions. The tension is built almost entirely through dialogue, long unbroken camera takes, and an incredibly precise sound design that makes the night feel alive and watchable.

What I really love about this movie is how it makes stillness feel tense. A long phone call, a quiet street, a voice crackling through static, and somehow all of it keeps you completely locked in. For a movie made on a low budget, The Vast of Night makes an entertaining watch.

You can watch The Vast of Night on Amazon Prime Video

HappyHorse 1.0 shot up to the top ranks in the Artificial Analysis leaderboard.

Chinese technology giant Alibaba’s cloud division led a $293m funding round into ShengShu Technology, a 2023-founded Beijing-based start-up behind the Vidu AI video-generation tool.

Baidu Ventures and Luminous Ventures also participated in the round. The company’s post-money valuation has not been disclosed.

The latest investment comes after ShengShu raised nearly $88m in a Series A round in February.

Vidu is marketed towards independent creators and animators, promising “effortless” production of content with “diverse artistic styles”.

The start-up is focusing on building ‘world models’ built on multimodal data such as audio, video and “touch”. The latest funding, the company said, will help support the development of a “general world model”.

The company’s latest Vidu Q3 Pro, which launched in January, places at the seventh rank on the Artificial Analysis leaderboard on text-to-video models, while making it to the 10th spot on the image-to-video rankings.

Vidu competes with other Chinese AI heavyweights, including ByteDance’s Seedance 2.0 and lead investor Alibaba’s own video model HappyHorse 1.0 that shot up to the top rank on the Artificial Analysis leaderboard.

Meanwhile, models from companies such as Singapore’s Skywork AI and Beijing-based Kuaishou, behind KlingAI, also rank high on the boards. These models are hungry to fill the gap in the video generation space left by OpenAI after it shuttered Sora late last month. Top leaderboard rankings are increasingly being filled by Chinese models.

HappyHorse was anonymously launched earlier this week before Alibaba claimed ownership today (10 April). The model is a product of Alibaba’s new Token Hub (ATH) innovation unit, placing number one on text-to-video and image-to-video ranks with no audio, while placing at the second spot with audio.

Bloomberg News reported that HappyHorse 1.0, which is under beta testing currently, will be followed up with more new ATH products. Alibaba’s share prices shot up following speculation that the company was behind the model.

Alibaba made the decision last month to bring its AI services and development works under a single roof called ATH, led by CEO Eddie Wu.

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Author: Saeed Abbasi, Senior Manager, Threat Research Unit, Qualys

With Time-to-Exploit now at negative seven days and autonomous AI agents accelerating threats, the data no longer supports incremental improvement. The architecture of defense must change.

What Leaders Need to Know

Analysis of CISA’s Known Exploited Vulnerabilities over the past four years shows critical vulnerabilities still open at Day 7 worsened from 56% to 63% despite teams closing 6.5x more tickets. Staffing cannot solve this.

Of the 52 tracked weaponized vulnerabilities in our study, 88% were patched more slowly than they were exploited — half were weaponized before any patch existed.

The problem is not speed. It is the operational model itself.

Cumulative exposure, not CVE counts, is the true risk metric that security teams now need to measure. While dashboards reward the sprint to get patches implemented, breaches exploit the tail. AI is not another attack surface — instead, the transition period where AI-powered attackers face human defenders is the industry’s most dangerous window.

In response, defenders have to implement their own autonomous, closed-loop risk operations.

The Broken Physics

New research from the Qualys Threat Research Unit, analyzing more than one billion CISA KEV remediation records from across 10,000 organizations over four years, quantifies what the industry has long suspected but never proved at scale. The operational model underpinning enterprise security is broken.

Vulnerability volumes have grown 6.5 times since 2022. According to Google M-Trends 2026, the average Time-to-Exploit has collapsed to negative seven days; in other words, adversaries are weaponizing the most serious vulnerabilities before patches exist. The percentage of critical vulnerabilities still open at seven days has climbed from 56 percent to 63 percent.

Yet this is not for lack of effort. Organizations closed 400 million more vulnerability events annually now than they did at baseline. Teams work harder, but it fails to make the difference where it counts. Our researchers call this the “human ceiling” — a structural limit no amount of staffing or process maturity can overcome. The constraint is not effort. It is the model itself.

Of 52 high-profile weaponized vulnerabilities tracked with complete exploitation timelines, 88 percent were remediated slower than they were exploited. As an example, Spring4Shell was exploited two days before disclosure, yet the average enterprise needed 266 days to remediate.

Similarly, the flaw in Cisco IOS XE was weaponized a month early; average close was 263 days.

The attacker’s advantage was measured in days. The defender’s response was measured in seasons. This is not an intelligence failure. It is an operationalization failure.

The Manual Tax and Risk Mass

The report identifies a “Manual Tax” — the multiplier effect where long-tail assets that human processes cannot reach drag exposure from weeks into months. For Spring4Shell, average remediation was 5.4 times the median.

The median tells a manageable story. The average tells the truth. Infrastructure systems face a harsher reality: for Cisco IOS XE, even the median was 232 days — compared to endpoint medians consistently under 14. When the best-case outcome is eight months, the Manual Tax is no longer a multiplier. It is the baseline.

Looking at average figures is no longer helpful for decision-making. Instead, looking at Risk Mass — vulnerable assets multiplied by days exposed — captures what CVE counts obscure around cumulative exposure. A companion metric, Average Window of Exposure (AWE), measures the full duration from weaponization to remediation across the environment.

As an example, Follina was weaponized 30 days before disclosure with an average close at Day 55.

However, the AWE stretched to 85 days. While the blind spot before disclosure accounted for 36 percent of that 85 days, the long tail of patching accounted for a further 44 percent. In total, pre-disclosure and long tail together represent 80 percent. The sprint that gets measured makes up less than 20.

At the same time, of 48,172 vulnerabilities disclosed in 2025, only 357 were remotely exploitable and actively weaponized. Organizations are burning remediation cycles on theoretical exposure while genuinely exploitable gaps persist.

Why the Gap Will Widen

Cybersecurity has long operated as a derivative of technology shifts — Windows security followed Windows, cloud security followed cloud. Leading practitioners and investors now argue AI breaks that pattern. It is not merely a new surface to defend; it is a fundamental transformation of the adversary itself.

Offensive agents can already discover, weaponize, and execute faster than any human-staffed operation can respond. The remediation data proves humans cannot keep pace today. Autonomous AI ensures the gap will accelerate tomorrow.

The transition period — where AI-powered attackers face human-speed defenders — represents the industry’s most dangerous window, compounded by the structural vulnerabilities that dominate the near term: attack surfaces expanded beyond what teams can govern, identity sprawl that outpaces policy, and remediation workflows still built on manual execution.

The traditional scan-and-report model was built for lower volumes of CVEs and longer exploit timelines. What replaces it is an end-to-end Risk Operations Center: embedded intelligence arriving as machine-readable decision logic, active confirmation validating whether a vulnerability is actually exploitable in a specific environment, and autonomous action compressing response to the timescale the threat demands.

The objective is not to eliminate human judgment but to elevate it, shifting practitioners from tactical execution to governing the policies that direct their own autonomous systems.

The organizations already winning the physics gap are not winning with larger teams. They are winning because they have removed human latency from the critical path.

How Security Teams can close the Risk Gap

The scan-and-report model — discover, score, ticket, manually route — was built for lower volumes and longer exploit timelines.

What replaces it is an end-to-end Risk Operations Center: embedded intelligence arriving as machine-readable decision logic, active confirmation validating whether a vulnerability is actually exploitable in a specific environment, and autonomous action compressing response to the timescale the threat demands.

The objective is not to eliminate human judgment but to elevate it — shifting practitioners from tactical execution to governing the policies that direct autonomous systems. The organizations already winning the physics gap are not winning with larger teams. They are winning because they have removed human latency from the critical path.

Time-to-Exploit will not return to positive numbers. Vulnerability volume will not plateau. The reactive model has hit a hard mathematical ceiling.

The only remaining question is whether organizations will use the architecture to match the mathematics — before the window between human-scale defense and autonomous-scale offense closes for good.

Sponsored and written by Qualys.

from the the-italian-job dept

Last month, we discussed NVIDIA’s demo video for its forthcoming DLSS 5 technology and the controversy surrounding it. While I’m going to continue to be of the posture that an injection of nuance is desperately needed in the reaction to AI tools and the like, our comments section largely disagreed with me on that post. That’s cool, that’s what this place is for, and I still love you all.

But this post is not about DLSS 5. Rather, it’s about the video itself and how it was briefly taken down over automated copyright claims thanks to an Italian news channel. Please note that the source material here was written while the video was still down, but it has since been restored.

And now, here we are in April, and NVIDIA’s DLSS 5 announcement trailer is no longer available to watch on YouTube on the company’s official GeForce channel. And no, it’s not because NVIDIA is responding to the feedback and retooling the technology for a re-reveal or re-announcement; it’s now blocked on “copyright grounds.”

A clear mistake, but also one that highlights the limitations of Google’s automated system for YouTube. Apparently, the Italian television channel La7 included footage from the DLSS 5 reveal in a recent broadcast and has since copyrighted it. From there, essentially every video on YouTube with DLSS 5 trailer footage was issued a copyright strike and said to be in violation, with the videos taken down with the following message: “Video unavailable: This video contains content from La7, who has blocked it in your country on copyright grounds.”

Yes, this was clearly a mistake. But it’s a mistake that I’m frankly tired of hearing about, all while Google does absolutely nothing to iterate on its copyright process and systems to mitigate such mistakes. The examples of this very thing are so legion as to be laughable. Whether due to error or due to malicious intent, videos that include content from other videos for the purposes of reporting and commentary, which are then copyrighted and result in takedowns of the source material, happens all the damned time.

This is almost certainly all automated, which means there are no human eyes looking for an error in the flagging of a copyright violation. It just gets tagged as such and taken down. And, no, the irony is not lost on me that we need human eyes to keep an automated copyright takedown on a video about AI from occurring.

What makes this alarming is that the video was taken down with seemingly no human interaction or input, as it’s clear that NVIDIA not only created DLSS 5, for better or worse, but also the trailer that has been a hot topic of discussion this year. We’re assuming this will be resolved fairly quickly. Still, it will be interesting to see whether YouTube responds to this case and claims that false copyright infringement notices like this are prevalent on the platform.

Google hasn’t been terribly interested in commenting on the plethora of cases like this in the past, so I strongly doubt it will now. Which is a damned shame, honestly, because the company really should be advocating for all of the users on its platform, if not especially those that are negatively impacted by this haphazard process.

But, for now, the video is back, so you can go hate-watch it again if you like.

Filed Under: copyright, dlss 5, geforce, takedowns, video games

Companies: la7, nvidia, youtube

OpenAI has rolled out a new Pro subscription that costs $100 and is in line with Claude’s pricing, which also has a $100 subscription, in addition to the $200 Max monthly plan.

Until now, OpenAI has offered three subscription tiers.

First is Go, which costs approx $8, second is Plus for $20, and then the final tier is at $200, a jump of $180.

On the other hand, Anthropic does not offer an $8 subscription, but it has a $100 subscription that comes between the cheapest $20 and the expensive $200 subscription, and it works for the company because it caters to the coding audience.

OpenAI has realized that it needs to go after coders and enterprises, similar to Anthropic’s strategy.

The company’s answer is ChatGPT Pro, which is designed for people who rely on AI to get high-stakes, complex work done for $100.

After this change, OpenAI’s offering looks like the following:

• Plus $20 – For lighter use. Try advanced capabilities like Codex and Deep Research for select projects throughout the week.
• Pro $100 – Built for real projects. For those who use advanced tools and models throughout the week, with 5x higher limits than Plus (and 10x Codex usage vs. Plus for a limited time).
• Pro $200 – For heavy lifting. Run your most demanding workflows continuously, even across parallel projects, with 20× higher limits than Plus.

All Pro plans include access to advanced features, including:

• Pro models
• Codex
• Deep research
• Image creation
• Memory
• File uploads

OpenAI says the Pro plan also includes unlimited access to GPT-5 and legacy models, but it’s not truly unlimited because the typical “Terms of Use” policies apply, including sharing of accounts.

Automated pentesting proves the path exists. BAS proves whether your controls stop it. Most teams run one without the other.

This whitepaper maps six validation surfaces, shows where coverage ends, and provides practitioners with three diagnostic questions for any tool evaluation.

Get Your Copy Now

A 27-year-old bug sat inside OpenBSD’s TCP stack while auditors reviewed the code, fuzzers ran against it, and the operating system earned its reputation as one of the most security-hardened platforms on earth. Two packets could crash any server running it. Finding that bug cost a single Anthropic discovery campaign approximately $20,000. The specific model run that surfaced the flaw cost under $50.

Anthropic’s Claude Mythos Preview found it. Autonomously. No human guided the discovery after the initial prompt.

The capability jump is not incremental

On Firefox 147 exploit writing, Mythos succeeded 181 times versus 2 for Claude Opus 4.6. A 90x improvement in a single generation. SWE-bench Pro: 77.8% versus 53.4%. CyberGym vulnerability reproduction: 83.1% versus 66.6%. Mythos saturated Anthropic’s Cybench CTF at 100%, forcing the red team to shift to real-world zero-day discovery as the only meaningful evaluation left. Then it surfaced thousands of zero-day vulnerabilities across every major operating system and every major browser, many one to two decades old. Anthropic engineers with no formal security training asked Mythos to find remote code execution vulnerabilities overnight and woke up to a complete, working exploit by morning, according to Anthropic’s red team assessment.

Anthropic assembled Project Glasswing, a 12-partner defensive coalition including CrowdStrike, Cisco, Palo Alto Networks, Microsoft, AWS, Apple, and the Linux Foundation, backed by $100 million in usage credits and $4 million in open-source grants. Over 40 additional organizations that build or maintain critical software infrastructure also received access. The partners have been running Mythos against their own infrastructure for weeks. Anthropic committed to a public findings report “within 90 days,” landing in early July 2026.

Security directors got the announcement. They didn’t get the playbook.

“I’ve been in this industry for 27 years,” Cisco SVP and Chief Security and Trust Officer Anthony Grieco told VentureBeat in an exclusive interview at RSAC 2026. “I have never been more optimistic for what we can do to change security because of the velocity. It’s also a little bit terrifying because we’re moving so quickly. It’s also terrifying because our adversaries have this capability as well, and so frankly, we must move this quickly.”

Security directors saw this story told fifteen different ways this week, including VentureBeat’s exclusive interview with Anthropic’s Newton Cheng. As one widely shared X post summarizing the Mythos findings noted, the model cracked cryptography libraries, broke into a production virtual machine monitor, and gave engineers with zero security training working exploits by morning. What that coverage left unanswered: Where does the detection ceiling sit in the methods they already run, and what should they change before July?

Seven vulnerability classes that show where every detection method hits its ceiling

1. OpenBSD TCP SACK, 27 years old. Two crafted packets crash any server. SAST, fuzzers, and auditors missed a logic flaw requiring semantic reasoning about how TCP options interact under adversarial conditions. Campaign cost ~$20,000. Anthropic notes the $50 per-run figure reflects hindsight.

2. FFmpeg H.264 codec, 16 years old. Fuzzers exercised the vulnerable code path 5 million times without triggering the flaw, according to Anthropic. Mythos caught it by reasoning about code semantics. Campaign cost ~$10,000.

3. FreeBSD NFS remote code execution, CVE-2026-4747, 17 years old. Unauthenticated root from the internet, per Anthropic’s assessment and independent reproduction. Mythos built a 20-gadget ROP chain split across multiple packets. Fully autonomous.

4. Linux kernel local privilege escalation. Mythos chained two to four low-severity vulnerabilities into full local privilege escalation via race conditions and KASLR bypasses. CSA’s Rich Mogull noted Mythos failed at remote kernel exploitation but succeeded locally. No automated tool chains vulnerabilities today.

5. Browser zero-days across every major browser. Thousands identified. Some required human-model collaboration. In one case, Mythos chained four vulnerabilities into a JIT heap spray, escaping both the renderer and the OS sandboxes. Firefox 147: 181 working exploits versus two for Opus 4.6.

6. Cryptography library vulnerabilities (TLS, AES-GCM, SSH). Implementation flaws enabling certificate forgery or decryption of encrypted communications, per Anthropic’s red team blog and Help Net Security. A critical Botan library certificate bypass was disclosed the same day as the Glasswing announcement. Bugs in the code that implements the math. Not attacks on the math itself.

7. Virtual machine monitor guest-to-host escape. Guest-to-host memory corruption in a production VMM, the technology keeping cloud workloads from seeing each other’s data. Cloud security architectures assume workload isolation holds. This finding breaks that assumption.

Nicholas Carlini, in Anthropic’s launch briefing: “I’ve found more bugs in the last couple of weeks than I found in the rest of my life combined.”

VentureBeat’s prescriptive matrix

Attackers are faster. Defenders are patching once a year.

The CrowdStrike 2026 Global Threat Report documents a 29-minute average eCrime breakout time, 65% faster than 2024, with an 89% year-over-year surge in AI-augmented attacks. CrowdStrike CTO Elia Zaitsev put the operational reality plainly in an exclusive interview with VentureBeat. “Adversaries leveraging agentic AI can perform those attacks at such a great speed that a traditional human process of look at alert, triage, investigate for 15 to 20 minutes, take an action an hour, a day, a week later, it’s insufficient,” Zaitsev said. A $20,000 Mythos discovery campaign that runs in hours replaces months of nation-state research effort.

CrowdStrike CEO George Kurtz reinforced that timeline pressure on LinkedIn the same day as the Glasswing announcement. “AI is creating the largest security demand driver since enterprises moved to the cloud,” Kurtz wrote. The regulatory clock compounds the operational one. The EU AI Act’s next enforcement phase takes effect August 2, 2026, imposing automated audit trails, cybersecurity requirements for every high-risk AI system, incident reporting obligations, and penalties up to 3% of global revenue. Security directors face a two-wave sequence: July’s Glasswing disclosure cycle, then August’s compliance deadline.

Mike Riemer, Field CISO at Ivanti and a 25-year US Air Force veteran who works closely with federal cybersecurity agencies, told VentureBeat what he is hearing from the government. “Threat actors are reverse engineering patches, and the speed at which they’re doing it has been enhanced greatly by AI,” Riemer said. “They’re able to reverse engineer a patch within 72 hours. So if I release a patch and a customer doesn’t patch within 72 hours of that release, they’re open to exploit.” Riemer was blunt about where that leaves the industry. “They are so far in front of us as defenders,” he said.

Grieco confirmed the other side of that collision at RSAC 2026. “If you talk to an operational team and many of our customers, they’re only patching once a year,” Grieco told VentureBeat. “And frankly, even in the best of circumstances, that is not fast enough.”

CSA’s Mogull makes the structural case that defenders hold the long-term advantage: fix a vulnerability once and every deployment benefits. But the transition period, when attackers reverse-engineer patches in 72 hours and defenders patch once a year, favors offense.

Mythos is not the only model finding these bugs. Researchers at AISLE, an AI cybersecurity startup, tested Anthropic’s showcase vulnerabilities on small, open-weights models and found that eight out of eight detected the FreeBSD exploit. AISLE says one model had only 3.6 billion parameters and costs 11 cents per million tokens, and that a 5.1-billion-parameter open model recovered the core analysis chain of the 27-year-old OpenBSD bug. AISLE’s conclusion: “The moat in AI cybersecurity is the system, not the model.” That makes the detection ceiling a structural problem, not a Mythos-specific one. Cheap models find the same bugs. The July timeline gets shorter, not longer.

Over 99% of the vulnerabilities Mythos has identified have not yet been patched, per Anthropic’s red team blog. The public Glasswing report lands in early July 2026. It will trigger a high-volume patch cycle across operating systems, browsers, cryptography libraries, and major infrastructure software. Security directors who have not expanded their patch pipeline, re-scoped their bug bounty programs, and built chainability scoring by then will absorb that wave cold. July is not a disclosure event. It is a patch tsunami.

What to tell the board

Every security director tells the board “we have scanned everything.” Merritt Baer, CSO at Enkrypt AI and former Deputy CISO at AWS, told VentureBeat that the statement does not survive Mythos without a qualifier.

“What security leaders actually mean is: we have exhaustively scanned for what our tools know how to see,” Baer said in an exclusive interview with VentureBeat. “That’s a very different claim.”

Baer proposed reframing residual risk for boards around three tiers: known-knowns (vulnerability classes your stack reliably detects), known-unknowns (classes you know exist but your tools only partially cover, like stateful logic flaws and auth boundary confusion), and unknown-unknowns (vulnerabilities that emerge from composition, how safe components interact in unsafe ways). “This is where Mythos is landing,” Baer said.

The board-level statement Baer recommends: “We have high confidence in detecting discrete, known vulnerability classes. Our residual risk is concentrated in cross-function, multi-step, and compositional flaws that evade single-point scanners. We are actively investing in capabilities that raise that detection ceiling.”

On chainability, Baer was equally direct. “Chainability has to become a first-class scoring dimension,” she said. “CVSS was built to score atomic vulnerabilities. Mythos is exposing that risk is increasingly graph-shaped, not point-in-time.” Baer outlined three shifts security programs need to make: from severity scoring to exploitability pathways, from vulnerability lists to vulnerability graphs that model relationships across identity, data flow, and permissions, and from remediation SLAs to path disruption, where fixing any node that breaks the chain gets priority over fixing the highest individual CVSS.

“Mythos isn’t just finding missed bugs,” Baer said. “It’s invalidating the assumption that vulnerabilities are independent. Security programs that don’t adapt, from coverage thinking to interaction thinking, will keep reporting green dashboards while sitting on red attack paths.”

VentureBeat will update this story with additional operational details from Glasswing’s founding partners as interviews are completed.