Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The security industry killed CVE coverage as a credible metric, and it deserved to die. Vendors inflated the numbers for years in the name of depth, and nobody in the room had an incentive to ask whether they reflected real validated risk or just a longer signature list. So “CVE coverage is a vanity metric” became earned consensus. The question I keep coming back to is whether the autonomous pentesting era makes that consensus outdated.

I have watched the security industry run a very profitable game with abbreviations for the last decade. The simple way to do it is to invent a category, give it a cool catchy abbreviation, market it as the missing piece of the stack, and repeat. The greatest examples are CTEM, BAS, ASM, and EASM. Every one of them arrived promising to close the gap the last one left open, and every one of them ended up as a line item on a renewal spreadsheet that nobody at the buyer‘s side could confidently defend.

Large language models are now a core part of the software development lifecycle. The 2025 Stack Overflow Developer Survey found that 82% of developers used OpenAI’s GPT models in their work last year, and Google has reported that AI now writes over 25% of new code committed at the company. All of that rests on one assumption. The model understands what you asked, and its answer is accurate.

Your vulnerability report is sorted by severity. The adversary looking at the same environment is sorted by path. That mismatch is the whole problem. Open any scanner output, and you get a tidy hierarchy: criticals at the top, then highs, then a long tail of mediums and lows that most teams will never touch. To the person who wrote the ticket, that tail is noise. To someone who thinks in chains, it’s a roadmap. A page of “lows” is not a page of things you can ignore.

Somewhere in your vendor list, right now, there is a door you have never checked. You didn’t build it, you don’t hold the key, and yet if someone walks through it, the breach notification goes out on your letterhead. The numbers too aren’t subtle. Verizon’s 2026 Data Breach Investigations Report found that 48% of breaches now involve a third party, up from 30% just a year earlier, the sharpest rise the report has ever recorded. Your vendors are your attack surface now.

Some of the most dangerous vulnerabilities in modern web apps are the default features left switched on where they were never meant to be reachable. The first entry in our Findings from the AP series looks at an exposed actuator endpoint. On paper, this finding started the same way most do: an HTTP endpoint returned a 200. Nothing about that response looks unusual by itself. It’s the kind of line that gets logged, categorized, and moved past in most automated scans.

On 26/05/2026, a security researcher at Astra Security found a critical Remote Code Execution (RCE) vulnerability in UpSnap, a web-based wake-on-LAN(WoL). The root cause is an OS Command Injection vulnerability(CWE-78) that exists in UpSnap’s device management functionality due to unsafe template interpolation of the IP and MAC fields.

You ship to production every day while your last pentest happened 11 months ago. Just say that sentence out loud, and we ought to rest our entire case of autonomous pentesting for SaaS companies right there. Everything below is just the supporting evidence. The mismatch isn’t subtle. Your engineers deploy continuously, your infrastructure reshapes itself weekly, and your security validation still runs on a calendar designed for software that shipped twice a year.

Ask any security engineer what they actually think about their vulnerability scanner, and you will get a version of the same answer. They trust maybe 20% of what shows up in the patching queue. The rest gets a suspicious glance, and a slow death in a backlog. That is the real cost of a false positive. It is quiet, it compounds, and it hollows the tool out from the inside. It is also the reason autonomous pentesting came to replace hypotheses with confirmed exploits.

Most engineering teams believe they solved secret scanning the day they flipped on GitHub’s default toggle, but the game doesn’t stop there. In many cases, an overlooked leak can spiral into a severe data breach, especially when an LLM is connected to sensitive data, internal APIs, or production infrastructure. Exposed credentials are hot and always in demand, and there’s plenty left online for bad actors to hunt.