I spent the last few months running Z3 SMT formal verification against 3,500 code artifacts generated by GPT-4o, Claude, Gemini, Llama, and Mistral.

▎ Results:

▎ - 55.8% contain at least one proven vulnerability

▎ - 1,055 findings with concrete exploitation witnesses

▎ - GPT-4o worst at 62.4% — no model scores below 48%

▎ - 6 industry tools combined (CodeQL, Semgrep, Cppcheck...) miss 97.8%

▎ - Models catch their own bugs 78.7% in review — but generate them anyway

▎ Paper: https://arxiv.org/html/2604.05292v1

▎ GitHub: https://github.com/dom-omg/broken-by-default

Hi r/netsec community,

Q4 2025 data, monitoring dark web leak sites and criminal forums
throughout October–December 2025.

Numbers:
- 2,373 confirmed victims
- 125 active ransomware groups
- 134 countries, 27 industries

Group highlights:
- Qilin peaked at 481 attacks in Q4, up from 113 in Q1
- Cl0p skipped encryption entirely in most campaigns — pure data theft + extortion via Oracle EBS and Cleo zero-days
- 46.3% of activity attributed to smaller/unnamed groups — RaaS commoditization is real

CVEs exploited this quarter (with group attribution):

RCE:
- CVE-2025-10035 (Fortra GoAnywhere MFT) — Medusa
- CVE-2025-55182 (React Server Components) — Weaxor
- CVE-2025-61882 (Oracle E-Business Suite) — Cl0p
- CVE-2024-21762 (Fortinet FortiOS SSL VPN) — Qilin

Privilege Escalation:
- CVE-2025-29824 (Windows CLFS driver → SYSTEM) — Play

Auth Bypass:
- CVE-2025-61884 (Oracle E-Business Suite) — Cl0p
- CVE-2025-31324 (SAP NetWeaver, CVSS 10.0) — BianLian, RansomExx

Notable: DragonForce announced a white-label "cartel" model through underground forums. Operations linked to Scattered Spider suggest staged attack chains — initial access and ransomware deployment split between separate actors.

Full report
brandefense.io/reports/ransomware-trends-report-q4-2025/

With the news of Hundreds of orgs being compromised daily, I saw a really cool red team tool that trains for this exact scenario. Have you guys used this new white hat tool? Thinking about ditching KB4 and even using this for our red teams for access.

CVE-2026-34980 and CVE-2026-34990

AI coding tools are being shipped fast. In too many cases, basic security is not keeping up.

In our latest research, we found the same sandbox trust-boundary failure pattern across tools from Anthropic, Google, and OpenAI. Anthropic fixed and engaged quickly (CVE-2026-25725). Google did not ship a fix by disclosure. OpenAI closed the report as informational and did not address the core architectural issue.

That gap in response says a lot about vendor security posture.

We benchmarked Opus 4.6's ability to find simple C vulns and found that the model flags about 1 in 4 flaws -- with a very high false positive rate and lots of inconsistency from run to run. Techniques like judge agents and requiring the model to justify its results improve the results to some extent, but they're still not great.

I’ve been experimenting with Chrome DevTools Protocol primitives to build tools for reversing and debugging JavaScript at runtime.

The idea is to interact with execution by hooking functions without monkeypatching or modifying application code.

Conceptually, this is closer to a Frida-style instrumentation model (onEnter/onLeave handlers), but applied to the browser via CDP.

Early experiments include:

• attaching hooks to functions at runtime
• inspecting and modifying arguments and local variables
• overriding return values (unfortunately limited to sync functions due to CDP constraints)
• following return values to their consumer (best-effort / heuristic)
• conditional stepping (stepIn / stepOut / stepOver)

All implemented via CDP (debugger breakpoints + runtime evaluation), so this also works inside closures and non-exported code.

I’d really appreciate feedback — especially from people doing reverse engineering, bug bounty, or complex frontend debugging.