The researcher who found it says the vulnerability could have been chained with a prompt injection to exfiltrate data.
The post Anthropic Silently Patches Claude Code Sandbox Bypass appeared first on SecurityWeek.
As enterprises rush AI projects into production, security teams are increasingly being forced into reactive mode.
The post Caught Off Guard: Securing AI After It Hits Production appeared first on SecurityWeek.
Unit 42 analyzes TamperedChef malware clusters that use trojanized productivity apps and malvertising to deliver stealthy payloads to targets.
The post Tracking TamperedChef Clusters via Certificate and Code Reuse appeared first on Unit 42.
A monthly analysis of how artificial intelligence is used in illicit communities, based on Flashpoint proprietary intelligence and direct visibility into real threat actor environments.
Table Of Contents
A finance employee joins a video call with their CFO and several colleagues. The request is routine. The faces match. The voices sound authentic. Minutes later, $25 million is transferred—only to be discovered later that every participant on the call, except one, was AI-generated.
Techniques behind incidents like this—synthetic video, voice cloning, scripted interactions—are now being discussed openly in the same environments where threat actors exchange tools and methods. In April 2026 alone, Flashpoint analysts identified 2,328,958 posts discussing artificial intelligence in the context of illicit activity.
This volume reflects a larger shift: artificial intelligence is now deeply embedded across cybercrime ecosystems, influencing fraud, impersonation, social engineering, and access operations at scale. It shows up in how content is generated, how identities are replicated, and how workflows are executed and refined over time.
That’s why we created the monthly AI Threat Report to examine how threat actors are using artificial intelligence in real-world illicit environments. Drawing on Flashpoint proprietary intelligence and direct visibility into primary source communities across forums, marketplaces, and chat services, the report analyzes the tactics, tools, and operational patterns shaping malicious AI use. Analysis of April’s activity shows a focus on prompt-sharing, jailbreak methods, and alternative models that support fewer safeguards or moderation controls.
In April 2026, Flashpoint analysts identified 2,328,958 posts discussing artificial intelligence in the context of illicit activity across forums, marketplaces, and chat services.
The underlying activity was concentrated around a familiar set of use cases and workflows:
However, the emphasis within those discussions shifted in several places in April.
This activity points to a more mature stage of adoption. The focus is less on building entirely new tooling and more on improving reliability, portability, and ease of use within workflows that already exist.
That pattern shows up repeatedly across monitored sources. Users exchange prompts, repost working methods, and refine outputs through direct feedback. In many cases, the same underlying techniques continue circulating with only minor changes between platforms or communities.Looking across April activity helps identify which methods continue to generate demand, where threat actors are adapting around platform restrictions, and which workflows remain active across multiple environments.
AI-related activity in April remained concentrated on a small number of platforms, though the distribution shifted noticeably compared to March.
Telegram accounted for the majority of observed activity, with 1,395,075 posts tied to AI services and discussions. Reddit, GitHub Gist, Pastebin, Discord, and smaller forums accounted for significantly lower volumes.
The lower Telegram volume does not indicate reduced interest in AI-enabled activity. The platform continues to function as a primary distribution layer for prompts, jailbreak methods, fraud tooling, and service advertisements.
Across April, the same prompts, offers, and workflows appeared repeatedly across channels, often reposted with only minor adjustments. Sellers updated listings based on user feedback, while buyers requested revisions tied to specific outputs or platforms.
Other platforms served more targeted roles:
The activity remains connected across environments. Methods introduced in one community frequently reappear elsewhere, particularly when they produce reliable outputs or help users work around moderation controls.Tracking how these discussions move between sources helps identify which workflows continue to gain traction and which techniques are becoming more broadly operationalized.
Across April, Flashpoint analysts observed 63,763 posts advertising or discussing KYC bypass methods using artificial intelligence, including deepfake-enabled verification workflows.
The methods were active across Telegram channels dedicated to identity verification bypass services.
Posts continued to advertise:
Some offerings included guidance on how to adapt responses for specific platforms or verification requirements. Others promoted combinations of synthetic video, matching fake documentation, and AI-generated scripts designed to support impersonation attempts from start to finish.
The broader workflow remains consistent. AI supports how identities are replicated, how verification checks are navigated, and how fraud operations are scaled across different services.
This activity connects directly to the wider access ecosystem already observed across illicit communities. Stolen credentials, session tokens, phishing infrastructure, and AI-enabled impersonation methods increasingly operate alongside one another within the same workflows.
Across April, posts tied to these methods continued to show active refinement through user feedback, reposting, and platform-specific variations.
For security teams, this activity remains relevant at the control layer. Verification systems, onboarding workflows, and account recovery processes continue to be tested in the same environments where these methods are exchanged and improved.
Across April, discussions tied to malicious or unrestricted LLM usage focused heavily on jailbreak methods, prompt-sharing workflows, and access to alternative models perceived as less restricted than mainstream platforms.
Flashpoint analysts observed a significant increase in discussions related to VeniceAI, driven in part by newly created Reddit and Discord communities dedicated to the platform. The increase highlights continued interest in models that users believe operate with fewer safeguards or moderation controls than services like ChatGPT or Gemini.
The activity centers on usability and output reliability.
Posts reference:
Many of these prompts are shared in collections that include updates, revisions, or support channels. Users exchange feedback when prompts stop working, outputs degrade, or platforms introduce new restrictions. Updated versions frequently follow within short timeframes.
This type of activity reinforces how prompt engineering has developed into its own service layer across illicit communities. The focus is not limited to the underlying model itself, but to the ability to generate repeatable outputs that can be applied directly within fraud, phishing, or impersonation workflows.
Across April, the same prompt structures and jailbreak methods appeared repeatedly across multiple sources, often with only small adjustments tied to platform or target.
The emphasis remains on accessibility, portability, and ease of use rather than custom model development.
Across April, the same behaviors continued to appear across different environments with only minor variation.
Prompt libraries, jailbreak methods, phishing workflows, and identity verification bypass techniques circulated across Telegram channels, forums, Discord communities, and paste sites. The wording changed slightly between platforms, though the underlying structure and outputs remained consistent.
This reuse is visible in how content moves between sources. A jailbreak prompt shared in one channel appears elsewhere with revised wording or additional instructions. A phishing workflow posted to a forum is copied into a paste site and redistributed through Telegram. Users request modifications, test outputs, and repost updated versions when restrictions change or methods stop working.
That cycle appeared repeatedly throughout April.
The activity also showed strong feedback loops tied to usability. Discussions focused heavily on which prompts generated reliable outputs, which models produced fewer restrictions, and which workflows required the least adjustment before use.
Across monitored sources, the same operational priorities appeared consistently:
Looking across April activity reinforces how AI-enabled methods continue to mature through repetition, iteration, and distribution across connected communities.
The activity tracked in this report shows how artificial intelligence is being used in environments where techniques are developed, tested, and shared before they surface elsewhere.
Across these communities, methods tied to fraud, impersonation, and access are reused, adjusted, and circulated in forms that others can apply directly. That process does not require significant change to move from discussion into use.
For security teams, the priority is maintaining visibility into how these methods are evolving and where they are being applied. That visibility supports earlier detection, more focused response, and a clearer understanding of which techniques are actively in circulation.
Monitoring these sources provides that context. It connects observed activity to the methods behind it and helps teams track how those methods develop over time.
If you want to see how this activity maps to your environment, request a demo.
The post AI Threat Report: How Artificial Intelligence Is Used Across Illicit Communities appeared first on Flashpoint.
Learn how AI transforms cybersecurity through enhanced threat detection, new attack methods, model vulnerabilities, and the evolving skills teams need in 2026.
The post Top AI Risks Every Security Team Should Be Testing For appeared first on OffSec.
While many companies are intentionally rolling out AI to boost quality and efficiency, unsanctioned AI tools are cropping up in corporate environments even faster. Software vendors are baking AI right into products companies already use (think Microsoft Copilot and Google Gemini), while employees are taking matters into their own hands and installing tools on the sly. As a result, businesses are staring down a poorly managed data leak channel: staff paste information from corporate systems into AI chatbots, sending data not just to the SaaS vendor, but straight to the developers behind the underlying AI model. Both the risks and the mitigation strategies vary depending on the type of AI system in play. We break down this broad topic, focusing heavily on tools for spotting and blocking AI at two distinct levels.
Depending on the type of AI in question, managing and blocking its use requires a different playbook. It’s essential to break down AI into four distinct categories:
Every company, depending on its industry, appetite for innovation, and risk tolerance, needs to draw its own line in the sand between recommended, approved case-by-case, and completely banned use cases for specific AI products. Regulated sectors like healthcare play by one set of rules, while retail businesses operate under an entirely different playbook. Either way, after analyzing exactly which AI tools have already slipped into the organization, corporate policies need to be fine-tuned. That’s why the first order of business is employing existing infosec and logging tools to scan corporate infrastructure.
Depending on the chosen strategy, the uncovered AI systems can be:
Spotting AI requires a multi-layered approach, as different detection methods complement each other and work best against specific types of AI.
| Technology | What it can detect |
| DNS | Any AI tool with an identifiable domain |
| Web Gateway or NGFW | Any AI tool with a recognizable request-and-response fingerprint (API endpoint paths, domains, and other indicators). Web filters can inspect traffic content, and many gateways/NGFWs now feature a dedicated category for detecting and blocking generative AI |
| EPP/EDR | Locally deployed LLMs (running via Ollama, LM Studio, and similar shells), native desktop apps for ChatGPT or Claude, agentic browsers, and open-source AI agents. An indirect but strong red flag is the presence of Node.js, Python, Git, Docker, or other containerization tools on machines belonging to non-technical staff |
| Application control | Similar to EPP/EDR, this allows to immediately block unwanted applications right out of the gate |
| Browser control | AI-focused browser extensions and visits to AI-themed websites. This is a lifesaver if the corporate web gateway can’t inspect encrypted traffic |
| SaaS Security Posture Management (SSPM) / Identity Governance | OAuth permissions requested by AI apps and services, as well as any third-party integrations plugging into core productivity hubs (Microsoft 365, Google Workspace, and others) |
Naturally, almost all of these tools allow to do more than just spot AI — they let to block it entirely, or at the very least, sound the alarm for the team in charge.
Popular office AI solutions — especially meeting assistants, email and calendar automation agents, and the like — gain access to corporate data by requesting OAuth permissions directly from communication, document workflow, or video conferencing platforms. If a user has the green light to grant these permissions to third-party apps, the resulting data leaks completely bypass the organization’s perimeter. Tools like EDR and NGFW won’t see a thing when a tool like Read.ai grabs recordings of every single meeting in, say, Microsoft Teams.
The most drastic — and often best — move is to block standard users from granting OAuth consent in the first place. Here’s how to handle the technical heavy lifting (Global Administrator, Application Administrator, or equivalent rights are needed):
In the Microsoft Entra admin center, head over to Identity > Applications > Enterprise apps > Consent and permissions > User consent settings. There User consent for applications can be disabled (check out Microsoft’s full guide).
In the Google Admin console, navigate to Security > Access and data control > API controls. Under Manage App Access, the trust level for all apps can be set: Trusted, Limited, Specific Google data, or Blocked. However, the real kicker here is the Unconfigured app settings subsection, which dictates what happens when a user tries to connect an unknown app. To seal this loophole, select Don’t allow users to access any third-party apps.
A separate subsection, Manage Google Services, permits fine-tuning exactly how third-party apps interact with Google Workspace and Google Cloud services. This allows to cut off access for each individual Google product (see Google’s official guide).
In Setup, use the Quick Find box to search for connected apps, then select Manage Connected Apps from the results. While settings are configured for each external app individually, all users can approve access by default. There isn’t a blanket block switch here; instead, Salesforce allows to opt for Admin approved users are pre-authorized (see the full Salesforce guide on this).
From the Admin settings menu, head to Apps and workflows -> App Management Settings. Tweak the Require approved apps setting by selecting Only allow pre-approved apps. Once that’s locked in, double-check that no rogue AI tools have slipped onto the approved list.
If you’re worried about deepfake likenesses of yourself showing up online, you’re not alone; YouTube is worried for you. It wants to protect you by having you upload a selfie video and government ID to its site.
The idea is that the video giant will use its own AI to patrol the service for fake videos using your likeness. In exchange, you get the chance to have them taken down.
This isn’t available for everyone, though. It’s for celebs, those in vulnerable jobs, and now, most YouTube creators.
YouTube has been working on this concept, which it calls its “likeness detection” system, since it first floated the idea publicly in September 2024. That December, it launched a partnership with the Creative Artists Agency that saw it using the technology with sporting and entertainment figures.
In October last year, it expanded likeness detection to cover more creators, and then in March it expanded it again to cover politicians and journalists. And last month, it widened the net again, offering the service to Hollywood celebs. They can use it regardless of whether they have a YouTube account, it added.
Now, in its latest move, anyone 18 or older with a selfie and ID can sign up. At least in theory, as it hasn’t rolled out to everyone yet. It’s also for faces only; AI-generated voice clones are another problem entirely.
Privacy advocates warned that YouTube’s likeness detection system could normalize handing biometric data to large tech platforms, even if YouTube says the data is only used to improve likeness detection models with creator permission.
On the help page for the likeness detection service, YouTube says creators can separately choose whether their face and voice templates are used to improve its likeness detection models.
“When you sign up for Likeness detection, you also have the option to allow YouTube to use your face and voice templates to develop and improve likeness detection models. This helps us build better, more accurate likeness detection technologies.”
Adding:
“You can opt out of YouTube’s use of this data for development and improvement of likeness models at any time.”
YouTube supports legislation intended to tackle deepfakes, such as the NO FAKES and TAKE IT DOWN acts. These are designed to help stop the misappropriation of someone’s image online. TAKE IT DOWN, which became law a year ago, focuses purely on “nonconsensual intimate imagery.” But that doesn’t cover other kinds of deepfakes, such as fake politicians or celebrity endorsements. Those are becoming increasingly common. NO FAKES, which hasn’t yet become law, is far broader in scope, assigning people federal rights over their own image.
Deepfakes, intimate and otherwise, are definitely a threat, especially for YouTubers who become popular. And the barrier to entry is lowering all the time. Google’s own DeepMind researchers found most generative AI misuse isn’t sophisticated; it’s mundane likeness manipulation by anyone with a browser.
So do you hand over your face and government ID for your protection, to a company whose broader data collection practices have faced years of scrutiny, and hope its policies don’t change? Or do you skip it and hope that the deepfake merchants don’t decide to target you?
Creators commenting on YouTube’s video revealing the service six months ago were less than impressed. One commenter said:
“I was 100% on board, up until the ID upload. That makes me very uncomfortable.”
Echoing several others who complained that it’s difficult to get takedown requests actioned, another added:
“If YouTube actually acted upon these kinds of reports, then I’d be more in favour of this.”
Whether you decide to sign up for the service or not, just be sure to do it with your eyes open.
If you’re worried about deepfake likenesses of yourself showing up online, you’re not alone; YouTube is worried for you. It wants to protect you by having you upload a selfie video and government ID to its site.
The idea is that the video giant will use its own AI to patrol the service for fake videos using your likeness. In exchange, you get the chance to have them taken down.
This isn’t available for everyone, though. It’s for celebs, those in vulnerable jobs, and now, most YouTube creators.
YouTube has been working on this concept, which it calls its “likeness detection” system, since it first floated the idea publicly in September 2024. That December, it launched a partnership with the Creative Artists Agency that saw it using the technology with sporting and entertainment figures.
In October last year, it expanded likeness detection to cover more creators, and then in March it expanded it again to cover politicians and journalists. And last month, it widened the net again, offering the service to Hollywood celebs. They can use it regardless of whether they have a YouTube account, it added.
Now, in its latest move, anyone 18 or older with a selfie and ID can sign up. At least in theory, as it hasn’t rolled out to everyone yet. It’s also for faces only; AI-generated voice clones are another problem entirely.
Privacy advocates warned that YouTube’s likeness detection system could normalize handing biometric data to large tech platforms, even if YouTube says the data is only used to improve likeness detection models with creator permission.
On the help page for the likeness detection service, YouTube says creators can separately choose whether their face and voice templates are used to improve its likeness detection models.
“When you sign up for Likeness detection, you also have the option to allow YouTube to use your face and voice templates to develop and improve likeness detection models. This helps us build better, more accurate likeness detection technologies.”
Adding:
“You can opt out of YouTube’s use of this data for development and improvement of likeness models at any time.”
YouTube supports legislation intended to tackle deepfakes, such as the NO FAKES and TAKE IT DOWN acts. These are designed to help stop the misappropriation of someone’s image online. TAKE IT DOWN, which became law a year ago, focuses purely on “nonconsensual intimate imagery.” But that doesn’t cover other kinds of deepfakes, such as fake politicians or celebrity endorsements. Those are becoming increasingly common. NO FAKES, which hasn’t yet become law, is far broader in scope, assigning people federal rights over their own image.
Deepfakes, intimate and otherwise, are definitely a threat, especially for YouTubers who become popular. And the barrier to entry is lowering all the time. Google’s own DeepMind researchers found most generative AI misuse isn’t sophisticated; it’s mundane likeness manipulation by anyone with a browser.
So do you hand over your face and government ID for your protection, to a company whose broader data collection practices have faced years of scrutiny, and hope its policies don’t change? Or do you skip it and hope that the deepfake merchants don’t decide to target you?
Creators commenting on YouTube’s video revealing the service six months ago were less than impressed. One commenter said:
“I was 100% on board, up until the ID upload. That makes me very uncomfortable.”
Echoing several others who complained that it’s difficult to get takedown requests actioned, another added:
“If YouTube actually acted upon these kinds of reports, then I’d be more in favour of this.”
Whether you decide to sign up for the service or not, just be sure to do it with your eyes open.
Four vulnerabilities in OpenClaw can be chained together to steal credentials, escape the sandbox, and plant persistent backdoors.
The post ‘Claw Chain’ OpenClaw Flaws Allow Sandbox Escape, Backdoor Delivery appeared first on SecurityWeek.
This week on the Lock and Code podcast…
In May of last year, a warning about AI came from somewhere unexpected: The Auschwitz-Birkenau State Museum.
Posting publicly on social media, the museum warned about a Facebook account using generative AI to create fake images of people who died in the Holocaust. Despite using AI to generate fake images, the people in said images were sometimes real. They had real names, birthplaces, and stories of deportation that the Auschwitz-Birkenau State Museum itself had shared before. They had real faces captured in real surviving photographs, which were likely abused to generate the false images.
In other words, someone, or some team of people online, was deepfaking the Holocaust.
As the Auschwitz museum wrote online:
“These are not real photos of the victims. They are digital inventions, often stylized or sanitized, that risk turning remembrance into fictionalized performance. The history of Auschwitz is a well-documented story. Altering its visual record with AI imagery introduces distortion, no matter the intent.”
Months later, the public found out what that intent was: money.
A BBC investigation found an international network of Facebook accounts posting AI-generated images to earn money from those images’ potential virality. It’s a problem sometimes referred to as “AI slop” but it comes with a major incentive. When accounts that make these kinds of images are invited to Facebook’s content monetization program, they can make $1,000 a month for posting anything that gets clicks.
And on Facebook, the BBC found, that means several accounts posting AI-generated images about the Holocaust. As the BBC reported:
“AI spammers have posted fake images purporting to be from inside [Auschwitz], such as a prisoner playing a violin or lovers meeting at the boundaries of fences—attracting tens of thousands of likes and shares.”
The economics of lying are concrete today. People can use AI to make fake images that make people feel good about terrible things or feel scared about untrue things, and they can make money until shut down by the Big Tech platforms themselves, which, in this case, only happened because of the BBC’s investigation. In fact, it’s that type of inaction from social media platforms that compelled the German government and multiple Holocaust memorial institutions to send an open letter earlier this year that asked for better controls and restrictions against this type of content.
As the signatories warned in their letter, the economic appeal for these accounts to distort history is too high a risk to allow. You can read the full letter here.
Today, on the Lock and Code podcast with host David Ruiz, we speak with Clara Mansfeld, a historian working on digital communications at one of the institutions signed onto the open letter—the Foundation of Hamburg Memorials and Learning Centers Commemorating the Victims of Nazi Crimes. In their conversation, Mansfeld discusses digital access to history, the manipulation of factual records through AI-generated imagery, and the threat that society faces when it becomes harder to evaluate the truth.
“What happens when the first thought we have with every historical image is, ‘Is that even real or is that AI?’ I don’t think we have really grasped what that means for us as a society.”
Tune in today to listen to the full conversation.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
Listen up—Malwarebytes doesn’t just talk cybersecurity, we provide it.
Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium Security for Lock and Code listeners.
This week on the Lock and Code podcast…
In May of last year, a warning about AI came from somewhere unexpected: The Auschwitz-Birkenau State Museum.
Posting publicly on social media, the museum warned about a Facebook account using generative AI to create fake images of people who died in the Holocaust. Despite using AI to generate fake images, the people in said images were sometimes real. They had real names, birthplaces, and stories of deportation that the Auschwitz-Birkenau State Museum itself had shared before. They had real faces captured in real surviving photographs, which were likely abused to generate the false images.
In other words, someone, or some team of people online, was deepfaking the Holocaust.
As the Auschwitz museum wrote online:
“These are not real photos of the victims. They are digital inventions, often stylized or sanitized, that risk turning remembrance into fictionalized performance. The history of Auschwitz is a well-documented story. Altering its visual record with AI imagery introduces distortion, no matter the intent.”
Months later, the public found out what that intent was: money.
A BBC investigation found an international network of Facebook accounts posting AI-generated images to earn money from those images’ potential virality. It’s a problem sometimes referred to as “AI slop” but it comes with a major incentive. When accounts that make these kinds of images are invited to Facebook’s content monetization program, they can make $1,000 a month for posting anything that gets clicks.
And on Facebook, the BBC found, that means several accounts posting AI-generated images about the Holocaust. As the BBC reported:
“AI spammers have posted fake images purporting to be from inside [Auschwitz], such as a prisoner playing a violin or lovers meeting at the boundaries of fences—attracting tens of thousands of likes and shares.”
The economics of lying are concrete today. People can use AI to make fake images that make people feel good about terrible things or feel scared about untrue things, and they can make money until shut down by the Big Tech platforms themselves, which, in this case, only happened because of the BBC’s investigation. In fact, it’s that type of inaction from social media platforms that compelled the German government and multiple Holocaust memorial institutions to send an open letter earlier this year that asked for better controls and restrictions against this type of content.
As the signatories warned in their letter, the economic appeal for these accounts to distort history is too high a risk to allow. You can read the full letter here.
Today, on the Lock and Code podcast with host David Ruiz, we speak with Clara Mansfeld, a historian working on digital communications at one of the institutions signed onto the open letter—the Foundation of Hamburg Memorials and Learning Centers Commemorating the Victims of Nazi Crimes. In their conversation, Mansfeld discusses digital access to history, the manipulation of factual records through AI-generated imagery, and the threat that society faces when it becomes harder to evaluate the truth.
“What happens when the first thought we have with every historical image is, ‘Is that even real or is that AI?’ I don’t think we have really grasped what that means for us as a society.”
Tune in today to listen to the full conversation.
Show notes and credits:
Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/
Outro Music: “Good God” by Wowa (unminus.com)
Listen up—Malwarebytes doesn’t just talk cybersecurity, we provide it.
Protect yourself from online attacks that threaten your identity, your files, your system, and your financial well-being with our exclusive offer for Malwarebytes Premium Security for Lock and Code listeners.
Recent news had us wondering whether Meta actually knows what it wants.
On one platform, Meta is promoting AI chats that it says even it cannot read. On another, it has removed one of the few features that genuinely prevented Meta from accessing private conversations.
“Meta removed support for end-to-end encrypted chats from Instagram as of May 8, 2026.”
At the moment, Meta is heavily promoting a new Incognito Chat mode for its Meta AI assistant in WhatsApp, built on top of a system it calls Private Processing. According to WhatsApp’s own announcement, Incognito Chat is:
“Truly private — no one can read your conversation, not even us.”
When you start an Incognito chat with Meta AI, you get a temporary conversation where messages aren’t saved and disappear by default, which Meta pitches as “a space to think and explore ideas without anyone watching.”
BBC News and others report that these AI chats are text‑only for now, run in a sandboxed environment, and are separate from your regular end‑to‑end encrypted (E2EE) messaging with other people on WhatsApp.
Meta is also preparing “Side Chat,” which will let you invoke Meta AI inside other WhatsApp chats, again using this Private Processing infrastructure to claim AI assistance without breaking the underlying encryption.
On paper, that’s an impressive technical and marketing story: powerful AI, wrapped in layers of privacy‑preserving infrastructure, added to an app that already has a strong reputation for end‑to‑end encryption by default.
Now contrast that with what’s happening on Instagram. On 8 May 2026, Meta removed optional end‑to‑end encryption for Instagram Direct Messages (DMs) entirely. Users who had previously turned the feature on were shown notices that “end‑to‑end encrypted messaging on Instagram is no longer supported as of 8 May 2026,” and were urged to download backups of their encrypted conversations before the cutoff.
End‑to‑end encryption ensures that only the sender and recipient can read their conversations. Instagram offered this as an opt‑in feature since late 2023, but it was buried several taps deep inside individual conversation settings and never turned on by default. Meta’s explanation for shutting it down is that “very few people” used encrypted DMs and that maintaining a separate encrypted system added complexity. Critics have pointed out the circular logic. The company hid the feature, did not advertise it, and is now using low adoption as the reason to kill it rather than, say, making it easier to find or turning it on by default.
From a user’s perspective, the result is confusing: one Meta product introduces stronger privacy than ever for AI chats, while another removes the one feature that truly stopped Meta from reading your conversations.
The key point to remember here is that “incognito” and “private” are marketing words, while end‑to‑end encryption is a technical guarantee.
For security‑conscious users, this split personality means you can no longer treat all Meta chats the same. WhatsApp remains end‑to‑end encrypted for person‑to‑person messages and adds optional privacy features around its AI, while Instagram DMs should now be assumed readable by Meta and potentially accessible to law enforcement, advertisers, or attackers who gain access to Meta’s systems.
We’ve seen that AI chats have suddenly turned up in search results without users’ knowledge. So there definitely is a positive side to this new feature.
We also know there have been lawsuits against chatbot providers in cases where the outcome of an AI conversation led to very undesirable results. But how would you be able to provide evidence when messages auto-disappear?
Meta’s recent moves show that strong privacy features can be added where they support a strategic narrative and removed where they conflict with business or regulatory priorities. Users can’t control those decisions, but they can respond by choosing where they hold their most sensitive conversations and by assuming that if a chat isn’t end‑to‑end encrypted by default, it is ultimately readable by someone other than the people in it.
So, what’s a safe way to move forward?
Scammers know more about you than you think.
Malwarebytes Mobile Security protects you from phishing, scam texts, malicious sites, and more. With real-time AI-powered Scam Guard built right in.
Recent news had us wondering whether Meta actually knows what it wants.
On one platform, Meta is promoting AI chats that it says even it cannot read. On another, it has removed one of the few features that genuinely prevented Meta from accessing private conversations.
“Meta removed support for end-to-end encrypted chats from Instagram as of May 8, 2026.”
At the moment, Meta is heavily promoting a new Incognito Chat mode for its Meta AI assistant in WhatsApp, built on top of a system it calls Private Processing. According to WhatsApp’s own announcement, Incognito Chat is:
“Truly private — no one can read your conversation, not even us.”
When you start an Incognito chat with Meta AI, you get a temporary conversation where messages aren’t saved and disappear by default, which Meta pitches as “a space to think and explore ideas without anyone watching.”
BBC News and others report that these AI chats are text‑only for now, run in a sandboxed environment, and are separate from your regular end‑to‑end encrypted (E2EE) messaging with other people on WhatsApp.
Meta is also preparing “Side Chat,” which will let you invoke Meta AI inside other WhatsApp chats, again using this Private Processing infrastructure to claim AI assistance without breaking the underlying encryption.
On paper, that’s an impressive technical and marketing story: powerful AI, wrapped in layers of privacy‑preserving infrastructure, added to an app that already has a strong reputation for end‑to‑end encryption by default.
Now contrast that with what’s happening on Instagram. On 8 May 2026, Meta removed optional end‑to‑end encryption for Instagram Direct Messages (DMs) entirely. Users who had previously turned the feature on were shown notices that “end‑to‑end encrypted messaging on Instagram is no longer supported as of 8 May 2026,” and were urged to download backups of their encrypted conversations before the cutoff.
End‑to‑end encryption ensures that only the sender and recipient can read their conversations. Instagram offered this as an opt‑in feature since late 2023, but it was buried several taps deep inside individual conversation settings and never turned on by default. Meta’s explanation for shutting it down is that “very few people” used encrypted DMs and that maintaining a separate encrypted system added complexity. Critics have pointed out the circular logic. The company hid the feature, did not advertise it, and is now using low adoption as the reason to kill it rather than, say, making it easier to find or turning it on by default.
From a user’s perspective, the result is confusing: one Meta product introduces stronger privacy than ever for AI chats, while another removes the one feature that truly stopped Meta from reading your conversations.
The key point to remember here is that “incognito” and “private” are marketing words, while end‑to‑end encryption is a technical guarantee.
For security‑conscious users, this split personality means you can no longer treat all Meta chats the same. WhatsApp remains end‑to‑end encrypted for person‑to‑person messages and adds optional privacy features around its AI, while Instagram DMs should now be assumed readable by Meta and potentially accessible to law enforcement, advertisers, or attackers who gain access to Meta’s systems.
We’ve seen that AI chats have suddenly turned up in search results without users’ knowledge. So there definitely is a positive side to this new feature.
We also know there have been lawsuits against chatbot providers in cases where the outcome of an AI conversation led to very undesirable results. But how would you be able to provide evidence when messages auto-disappear?
Meta’s recent moves show that strong privacy features can be added where they support a strategic narrative and removed where they conflict with business or regulatory priorities. Users can’t control those decisions, but they can respond by choosing where they hold their most sensitive conversations and by assuming that if a chat isn’t end‑to‑end encrypted by default, it is ultimately readable by someone other than the people in it.
So, what’s a safe way to move forward?
Scammers know more about you than you think.
Malwarebytes Mobile Security protects you from phishing, scam texts, malicious sites, and more. With real-time AI-powered Scam Guard built right in.
Some AI-based video age-verification checks can be fooled with a fake mustache.
Last month, Anthropic made a remarkable announcement about its new model, Claude Mythos Preview: it was so good at finding security vulnerabilities in software that the company would not release it to the general public. Instead, it would only be available to a select group of companies to scan and fix their own software.
The announcement requires context—but it contained an essential truth.
While Anthropic’s model is really good at finding software vulnerabilities, so are other models. The UK’s AI Security Institute found that OpenAI’s GPT-5.5, already generally available, is comparable in capability. The company Aisle reproduced Anthropic’s published results with smaller, cheaper models.
At the same time, Anthropic’s refusal to publicly release its new model makes a virtue out of necessity. Mythos is very expensive to run, and the company doesn’t appear to have the resources for a general release. What better way to juice the company’s valuation than to hint at capabilities but not prove them, and then have others parrot their claims?
Nonetheless, the truth is scary. Modern generative AI systems—not just Anthropic’s, but OpenAI’s and other, open-source models—are getting really good at finding and exploiting vulnerabilities in software. And that has important ramifications for cybersecurity: on both the offense and the defense.
Attackers will use these capabilities to find, and automatically hack, vulnerabilities in systems of all kinds. They will be able to break into critical systems around the world, sometimes to plant ransomware and make money, sometimes to steal data for espionage purposes, and sometimes to control systems in times of hostility. This will make the world a much more dangerous, and more volatile, place.
But at the same time, defenders will use these same capabilities to find, and then patch, many of those same systems. For example, Mozilla used Mythos to find 271 vulnerabilities in Firefox. Those vulnerabilities have been fixed, and will never again be available to attackers. In the future, AIs automatically finding and fixing vulnerabilities in all software will be a normal part of the development process, which will result in much more secure software.
Of course, it’s not that simple. We should expect a deluge of both attackers using newly found vulnerabilities to break into systems, and at the same time much more frequent software updates for every app and device we use. But lots of systems aren’t patchable, and many systems that are don’t get patched, meaning that many vulnerabilities will stick around. And it does seem that finding and exploiting is easier than finding and fixing. All of this points to a more dangerous short-term future. Organizations will need to adapt their security to this new reality.
But it’s the long term that we need to focus on. Mythos isn’t unique, but it’s more capable than many models that have come before. And it’s less capable than models that will come after. AIs are much better at writing software than they were just six months ago. There’s every reason to believe that they will continue to get better, which means that they will get better at writing more secure software. The endgame gives AI-enhanced defenders advantages over AI-enhanced attackers.
Even more interesting are the broader implications. The same searching, pattern-matching and reasoning capabilities that make these models so good at analyzing software almost certainly apply to similar systems. The tax code isn’t computer code, but it’s a series of algorithms with inputs and outputs. It has vulnerabilities; we call them tax loopholes. It has exploits; we call them tax avoidance strategies. And it has black hat hackers: attorneys and accountants.
Just as these models are finding hundreds of vulnerabilities in complex software systems, we should expect them to be equally effective at finding many new and undiscovered tax loopholes. I am confident that the major investment banks are working on this right now, in secret. They’ve fed AI the tax code of the US, or the UK, or maybe every industrialized country, and tasked the system with looking for money-saving strategies. How many tax loopholes will those AIs find? Ten? One hundred? One thousand? The Double Dutch Irish Sandwich is a tax loophole that involves multiple different tax jurisdictions. Can AIs find loopholes even more complex? We have no idea.
Sure, the AIs will come up with a bunch of tricks that won’t work, but that’s where those attorneys and accountants come in—to verify, and then justify, the loopholes. And then to market them to their wealthy clients.
As goes the tax code, so goes any other complex system of rules and strategies. These models could be tasked with finding loopholes in environmental rules, or food and safety rules—anywhere there are complex regulatory systems and powerful people who want to evade those rules.
The results will be much worse than insecure computers. Tax loopholes result in less revenue collected by governments, and regulatory loopholes allow the powerful to skirt the rules, both of which have all sorts of social ramifications. And while software vendors can patch their systems in days, it generally takes years for a country to amend its tax code. And that process is political, with lobbyists pressuring legislators not to patch. Just look at the carried interest loophole, a US tax dodge that has been exploited for decades. Various administrations have tried to close the vulnerability, but legislators just can’t seem to resist lobbyists long enough to patch it.
AI technologies are poised to remake much of society. Just as the industrial revolution gave humans the ability to consume calories outside of their bodies at scale, the AI revolution will give humans the ability to perform cognitive tasks outside of their bodies at scale. Our systems aren’t designed for that; they’re designed for more human paces of cognition. We’re seeing it right now in the deluge of software vulnerabilities that these models are finding and exploiting. And we will soon see it in a deluge of vulnerabilities in all sorts of other systems of rules. Adapting to this new reality will be hard, but we don’t have any choice.
This essay originally appeared in The Guardian.
Schools love a good photo, whether it’s from a trip to a castle, a science prize ceremony, or sports day shot from three angles. For two decades, celebratory images like these have gone straight onto school websites, captioned with a name and a grade. But those days are gone, because it’s the internet in 2026 and we can’t have nice things.
As first reported by the Guardian, experts are now urging schools to take those pictures down. According to the UK’s National Crime Agency, the Internet Watch Foundation, and an advisory body called the Early Warning Working Group (EWWG), blackmailers have been scraping ordinary school photos, feeding them through AI deepfake tools to manufacture child sexual abuse material (CSAM), and demanding payment to keep the images offline.
Late last year, cybercriminals contacted an unnamed UK secondary school with that demand. The IWF classified 150 of the resulting images as CSAM under UK law and generated digital fingerprints for each image so major platforms could block reuploads.
The IWF isn’t naming the school or the police force, and it doesn’t believe this was an isolated case. The EWWG says it’s “only a matter of time” before more schools face similar demands.
UK safeguarding minister Jess Phillips called it a “deeply worrying emerging threat.” In February 2025, the UK became the first country to ban AI tools designed specifically to generate CSAM.
This threat didn’t appear overnight, and it isn’t limited to the UK. It’s an evolution of a long-time threat: sextortion, when someone uses intimate images to blackmail you. Traditionally, sextortion relied on real intimate images that were stolen or shared, but deepfake AI has changed everything.
The FBI’s Internet Crime Complaint Center (IC3) logged more than 16,000 sextortion complaints in the first half of 2021, with losses exceeding $8 million. By June 2023, the bureau warned the playbook had shifted: attackers were using ordinary social media photos to create fake explicit images and extort minors.
UK children’s counseling helpline Childline has seen similar shifts as deepfake tools become more accessible. It already logs many sextortion cases each year, many from kids who were manipulated into sharing intimate images of themselves. Now, the organization is getting calls from children who are being sent deepfake CSAM images of themselves without any prior contact.
One 15-year-old girl, for example, was sent a “really convincing” fake nude built from her Instagram photos.
By November 2025, IWF reports of AI-generated CSAM had more than doubled year over year, rising from 199 to 426. Girls accounted for 94% of the victims. Reported cases included children ranging from newborns to two-year-olds, according to the organization.
The ecosystem around these tools is industrial. In April 2025, a researcher found an exposed AWS S3 bucket belonging to South Korean “nudify” app GenNomis containing 93,485 AI-generated images alongside the prompts that produced them.
The EWWG’s advice is to replace close-up, identifiable photos with images taken from a distance, blurred images, or photos shot from behind. It also advises schools to remove full names from captions, audit existing images, and ask parents to re-sign consent forms.
In fact, it advises schools to rethink whether they need to publish children’s photos online at all.
Some schools have already acted. According to the Guardian, Loughborough Schools Foundation, a group of three private schools sharing a website, removed recognizable pupil images entirely last year.
The UK Information Commissioner’s Office (ICO) says that it “would still generally expect you to offer an opt-out to parents” when publishing an identifiable photo of a child, but says this isn’t legally the same as consent, which has a higher bar.
Things get murkier in the US, where states often have their own student privacy statutes. Broadly, though, under the Family Educational Rights and Privacy Act (FERPA), schools typically include identifiable photos of students under the category of directory information. This category also covers name, address, telephone listing, date and place of birth, participation in officially recognized activities and sports, and dates of attendance.
Under FERPA, schools can publish this type of information unless the child’s guardian specifically opts out. They have to notify a guardian when they want to publish it, but that process may not apply indefinitely after a student leaves the school.
That means student photos and information can remain online long after families assume they have disappeared.
Back in the UK, Childline’s Report Remove service allows children to flag explicit images or videos of themselves that have been posted online. The service took 394 blackmail reports from under-18s last year, up by one-third compared to 2024.
Meanwhile, the UK government is amending the Crime and Policing Bill, forcing platforms to take flagged intimate images down within 48 hours or face fines of 10% of global revenue.
We anticipate a race between regulators and AI-enabled cybercriminals. Right now, attackers still have to manually find the photos themselves. The concern is that this process could soon become automated, allowing criminals to scrape names and photos from school websites and social media platforms at scale.
For parents, the simplest protection may be limiting how many identifiable pictures of your children are available online. That includes being vigilant not just with your child’s school, but their sports clubs, extracurricular activities, and social media accounts.
Schools love a good photo, whether it’s from a trip to a castle, a science prize ceremony, or sports day shot from three angles. For two decades, celebratory images like these have gone straight onto school websites, captioned with a name and a grade. But those days are gone, because it’s the internet in 2026 and we can’t have nice things.
As first reported by the Guardian, experts are now urging schools to take those pictures down. According to the UK’s National Crime Agency, the Internet Watch Foundation, and an advisory body called the Early Warning Working Group (EWWG), blackmailers have been scraping ordinary school photos, feeding them through AI deepfake tools to manufacture child sexual abuse material (CSAM), and demanding payment to keep the images offline.
Late last year, cybercriminals contacted an unnamed UK secondary school with that demand. The IWF classified 150 of the resulting images as CSAM under UK law and generated digital fingerprints for each image so major platforms could block reuploads.
The IWF isn’t naming the school or the police force, and it doesn’t believe this was an isolated case. The EWWG says it’s “only a matter of time” before more schools face similar demands.
UK safeguarding minister Jess Phillips called it a “deeply worrying emerging threat.” In February 2025, the UK became the first country to ban AI tools designed specifically to generate CSAM.
This threat didn’t appear overnight, and it isn’t limited to the UK. It’s an evolution of a long-time threat: sextortion, when someone uses intimate images to blackmail you. Traditionally, sextortion relied on real intimate images that were stolen or shared, but deepfake AI has changed everything.
The FBI’s Internet Crime Complaint Center (IC3) logged more than 16,000 sextortion complaints in the first half of 2021, with losses exceeding $8 million. By June 2023, the bureau warned the playbook had shifted: attackers were using ordinary social media photos to create fake explicit images and extort minors.
UK children’s counseling helpline Childline has seen similar shifts as deepfake tools become more accessible. It already logs many sextortion cases each year, many from kids who were manipulated into sharing intimate images of themselves. Now, the organization is getting calls from children who are being sent deepfake CSAM images of themselves without any prior contact.
One 15-year-old girl, for example, was sent a “really convincing” fake nude built from her Instagram photos.
By November 2025, IWF reports of AI-generated CSAM had more than doubled year over year, rising from 199 to 426. Girls accounted for 94% of the victims. Reported cases included children ranging from newborns to two-year-olds, according to the organization.
The ecosystem around these tools is industrial. In April 2025, a researcher found an exposed AWS S3 bucket belonging to South Korean “nudify” app GenNomis containing 93,485 AI-generated images alongside the prompts that produced them.
The EWWG’s advice is to replace close-up, identifiable photos with images taken from a distance, blurred images, or photos shot from behind. It also advises schools to remove full names from captions, audit existing images, and ask parents to re-sign consent forms.
In fact, it advises schools to rethink whether they need to publish children’s photos online at all.
Some schools have already acted. According to the Guardian, Loughborough Schools Foundation, a group of three private schools sharing a website, removed recognizable pupil images entirely last year.
The UK Information Commissioner’s Office (ICO) says that it “would still generally expect you to offer an opt-out to parents” when publishing an identifiable photo of a child, but says this isn’t legally the same as consent, which has a higher bar.
Things get murkier in the US, where states often have their own student privacy statutes. Broadly, though, under the Family Educational Rights and Privacy Act (FERPA), schools typically include identifiable photos of students under the category of directory information. This category also covers name, address, telephone listing, date and place of birth, participation in officially recognized activities and sports, and dates of attendance.
Under FERPA, schools can publish this type of information unless the child’s guardian specifically opts out. They have to notify a guardian when they want to publish it, but that process may not apply indefinitely after a student leaves the school.
That means student photos and information can remain online long after families assume they have disappeared.
Back in the UK, Childline’s Report Remove service allows children to flag explicit images or videos of themselves that have been posted online. The service took 394 blackmail reports from under-18s last year, up by one-third compared to 2024.
Meanwhile, the UK government is amending the Crime and Policing Bill, forcing platforms to take flagged intimate images down within 48 hours or face fines of 10% of global revenue.
We anticipate a race between regulators and AI-enabled cybercriminals. Right now, attackers still have to manually find the photos themselves. The concern is that this process could soon become automated, allowing criminals to scrape names and photos from school websites and social media platforms at scale.
For parents, the simplest protection may be limiting how many identifiable pictures of your children are available online. That includes being vigilant not just with your child’s school, but their sports clubs, extracurricular activities, and social media accounts.
The financial services industry (FSI) is using AI to transform how financial institutions serve their customers. AI solutions can help proactively manage portfolios, automatically refinance mortgages when rates decrease, and negotiate insurance premiums for customers.
However, this adoption brings new governance, risk, and compliance (GRC) considerations that organizations need to address. To help FSI customers navigate these challenges, AWS is excited to announce an updated AWS User Guide to Governance, Risk, and Compliance for Responsible AI Adoption within Financial Services Industries.
This comprehensive guide provides FSI customers practical considerations for responsible AI adoption across key dimensions including governance, risk management, compliance, data management, model management and AI agent management. It includes detailed AWS service capabilities that customers can use to address these considerations, such as Amazon Bedrock AgentCore, Amazon Bedrock Guardrails, Amazon Bedrock Agents, Amazon SageMaker Autopilot, and Amazon SageMaker Model Monitor.
The guide is available at the AWS Whitepaper portal and is complementary to other AWS resources such as the AWS Responsible Use of AI Guide, AWS Cloud Adoption Framework for AI, AWS Well-Architected Framework – Responsible AI Lens, AWS Well-Architected Framework – Generative AI Lens, and AWS Well-Architected Framework – Machine Learning Lens.
As the regulatory environment and leading practices continue to evolve, we will provide further updates on the AWS Security Blog and AWS Compliance Center. You can also reach out to your AWS account team for help finding the resources you need.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.
Login
