Earlier this week our CEO and Founder @theonejvo spoke with @notLeonardCohen from @ACSnewsfeed's (Australian Computer Society)'s Information Age about the Five Eyes cyber security agencies' latest statement, a sober and needed direct warning about AI driven cyber risk from the alliance spanning Australia, New Zealand, the US, UK, and Canada. The message from Five Eyes is clear - AI is already here, it is lowering the barrier for malicious actors, and it is shrinking the window between vulnerability discovery and exploitation. Their assessment is that cyber risk assumptions can now go stale in months rather than years, and that defenders need to move faster and more deliberately to keep pace. Speaking with Information Age, Jamieson noted that frontier models are already compressing the attack lifecycle across discovery, chaining, and exploitation, while at the same time handing defenders powerful new tools. As he put it, the gap between what advanced offensive AI can do today versus traditional red teaming already speaks for itself. The Five Eyes are correctly flagging that defenders must match that speed and sophistication deliberately, or they will lose ground rapidly. He was also candid about where organisations go wrong. Delaying foundational hygiene such as patching, legacy system remediation, identity controls, and attack surface reduction, while hoping AI magic fixes everything, is a recipe for fast and expensive breaches. So what does getting this right actually look like? In Jamieson's words, good AI implementation means treating AI as a force multiplier for resilience and detection rather than a shortcut to efficiency. It is rigorously red teamed, ideally against other frontier offensive agents, integrated with strong human oversight and secure by design principles, and continuously pressure tested against real attack chains. Bad implementation is bolting on AI tools primarily for cost or speed gains without equivalent adversarial testing, and deploying agentic systems with broad tool access before the security model has caught up. This is the exact problem we built Aether AI to solve. As Australia's first dedicated frontier AI attack and defence platform, our agents continuously emulate sophisticated adversaries at machine speed, so defenders can shrink their disadvantage before attackers exploit it. The Five Eyes said it plainly. We must act now.

This week, our CEO and Co-Founder @theonejvo was interviewed by @sharongoldman of @FortuneMagazine for her in-depth article on a new research paper from the @UofT. The study demonstrates an autonomous AI-driven worm capable of adapting, reasoning, and spreading across a simulated corporate network with no human intervention-exploiting nearly three-quarters of the machines in just one week. In the conversation, Jamieson shared his perspective on what this development means for the cybersecurity landscape. He noted that while laboratory demonstrations like this one use intentionally vulnerable environments, the core capability is real and growing rapidly. AI is steadily lowering the barrier to building autonomous offensive tools, which is why he views the research as an important warning sign rather than a surprise. Jamieson emphasized that defenders still hold meaningful advantages-particularly in detecting the unusual network traffic and activity generated when worms move large AI models or operate across systems. However, he cautioned that this edge will erode as models become smaller and more efficient. Organisations cannot afford to rely solely on traditional patching timelines; they must become more precise in prioritising risks that truly enable attacker control. At @tryaether_ai, we continue to focus on building the next generation of defensive tools and red-teaming capabilities that help security teams stay ahead of these adaptive threats. This includes helping organizations simulate realistic adversarial scenarios, identify critical exposure points, and strengthen their resilience against increasingly sophisticated AI-augmented attacks. We're grateful to Sharon and the Fortune team for highlighting this important topic, and we look forward to continued dialogue across the industry on practical ways to defend against agentic AI threats. The future of cybersecurity will require both stronger software foundations and smarter, AI-powered defences-and we're committed at the frontier to ensure both evolve.

Our founder and CEO, @theonejvo, was cited in the @verge this week among the independent researchers impressed by @AnthropicAI Fable 5's launch-day protections. Here is what was tested, and why the industry measures the wrong thing. Most of the frontier cyber conversation rests on familiar tools: capture-the-flag puzzles, vulnerable practice environments, and codebases with a weakness already planted inside. If a model finds the bug or recites the technique, that is treated as danger. Yet this is exactly the material that leaks into training, so a high score can mean the model recalled a solution rather than worked one out. The jailbreak discourse repeats the error: it asks whether a model can be coaxed into talking about an exploit, describing a buffer overflow or producing attack code. Getting a chatbot to speak is a different problem from getting a model to act. A real intrusion is a long, coherent campaign that reaches far beyond spotting a vulnerability: gaining a foothold, holding it through persistence, escalating privilege, moving laterally, drawing data out, and delivering impact. Each stage depends on the last and demands the model hold the whole picture in mind, recover from dead ends, and keep its intent straight over dozens of turns against a system it has never seen. That sustained competence separates a model that can talk about hacking from one that can do it. Our testing probes exactly that. Jamieson ran Fable 5 through PolyRange, his own evaluation framework, which builds a wholly new, fictitious target each trial, its secret reachable only by completing a working attack. That closes the contamination loophole and forces real work across the full chain on unfamiliar ground. Across a long, many-step engagement, all 181 of the model's responses came from the weaker fallback, though every request named Fable. A controlled split confirmed it: benign queries to Fable, every offensive request to the weaker model. The safeguard stopped the chain every time the work turned hostile. We are candid about the limits. Jamieson made no concerted effort to defeat the safeguard, which others have since reported doing, though those reports describe eliciting a description or a single output. The harder question is whether a jailbroken model will coherently carry a complete attack to impact; public demonstrations have largely stopped at elicitation, and on Fable the chain broke each time. The test was small, covering Fable 5 alone, not the more powerful Mythos 5. Export policy should be shaped by the model that can be driven, start to finish, through a real-world attack, for which a model that merely talks is a weak proxy. Until the debate rests on contamination-free, end-to-end evaluation, it will keep mistaking recall for skill and speech for action.

It's now a race. Defenders hardening at machine speed vs AI-enabled adversaries weaponising at machine speed. The side that moves faster wins each vulnerability. The side that moves slower writes the breach report.

Recently, our CEO and co-founder @theonejvo spoke to @Jonathan1Gibson from @thedispatch about the growing debate around autonomous AI threats and what current research tells us about where this is headed. The conversation followed new work from @PalisadeAI showing that an AI model could autonomously hack into a server, copy itself onto the machine, and run there without human direction. One of the key points Jamieson raised was the sheer footprint of modern models. As he told The Dispatch, "As it stands, it's not a scalable, reliable attack if you have to push a 100-gigabyte AI brain around along with your malware every time you compromise a new system." Most robust, capable models today are heavy, power-hungry, and noisy in ways that make stealthy propagation across millions of machines impractical. That constraint, however, is eroding fast. Distillation, quantization, and mixture-of-experts architectures are shrinking models dramatically while preserving capability. A 7-billion parameter model today might match what a larger model could do eighteen months ago. At the same time, harnessing, post-training techniques, reasoning-focused training, and inference-time compute scaling are making smaller models meaningfully more effective. The result is a steady compression of capability into smaller and smaller footprints. That's why Jamieson was careful to note the underlying scenario is well within the realm of plausibility. The Palisade demonstration matters precisely because it shows the end-to-end capability exists in a research setting. The remaining gap is largely about efficiency, and efficiency is exactly what the entire industry is optimising for. A capable offensive agent that needs a powerful stack today might need a single consumer GPU in a few years, and a modest laptop after that. This is the space Aether AI works in every day. We build autonomous offensive AI to find and exploit vulnerabilities, so we have a clear view of both what these systems can do now and where the curve is heading. Today's threat landscape is still dominated by targeted ransomware against high-value organisations, a point echoed in the Dispatch piece by Marcus Hutchins (@MalwareTechBlog). Tomorrow's landscape could look meaningfully different, and defenders should be preparing for that shift rather than waiting for it to arrive.

Most human mobile penetration testers install your apps on an emulator, intercept traffic, hook the runtime (when they can jailbreak), bypass certificate pinning (or ask you to help disable it), extract credentials from memory, pivot to the backend, and then write it all up in a report. Aether AI does every single one of those things - on real physical iOS and Android hardware, through a dedicated device gateway, at a scale and speed and with the consistency that no human team can match (we know, we've benchmarked). When Aether extracts a token from runtime memory, it immediately tests it against the API. When it recovers a backend hostname from decrypted in-app traffic, it adds it to the asset graph and probes it across web, API, cloud, and identity surfaces in the same engagement. When a fix ships, it retests the original chain unlimited times with captured screenshots and evidence on every run. When the customer wants it, it generates the pull-request-ready code patch, the WAF rule, the SIEM detection content. A human pentester produces a report, while Aether AI produces the report, the chain, the retest, the patch, and the defensive artefacts - across mobile and backend in one continuous engagement. We published a new blog on our mobile penetration testing AI, link in comments.

Nine out of ten of the most significant, most damaging, most widely covered cyber attacks of the last two years required no zero day vulnerabilities. They required a compromised maintainer account, a credential harvested by an infostealer, a Citrix portal without MFA, a developer targeted with a convincing social engineering campaign, a known CVE that an organisation never got around to patching, a database left exposed because nobody checked. These are not obscure attack classes. They are the same classes that have dominated breach data for a decade, and they are the classes that AI-powered attack capability - including the AI our own agents use - makes dramatically more exploitable at scale.

After spending the last 15+ years hacking some of the worlds most "secure" targets. From Governments & battlefield technology, to banks and even casino's. We're soon releasing the worlds most dangerous attack AI - to help you fight back and stay far ahead of your adversaries.

In 1997, Garry Kasparov lost to Deep Blue. Today, a $50 phone running Stockfish beats every grandmaster alive. The gap has nothing to do with skill alone. It's a combination of creativity, scale and persistence. We built @tryaether_ai because adversaries already have this.