Commvault plugs holes in backup suite that allow remote code executio
Source: https://www.helpnetsecurity.com/2025/08/20/commvault-backup-suite-vulnerabilities-fixed/
Guess Who Would Be Stupid Enough To Rob The Same Vault Twice? Pre-Auth RCE Chains in Commvault - watchTowr Labs
Source: https://labs.watchtowr.com/guess-who-would-be-stupid-enough-to-rob-the-same-vault-twice-pre-auth-rce-chains-in-commvault/?123
How We Exploited CodeRabbit: From a Simple PR to RCE and Write Access on 1M Repositories
Source: https://research.kudelskisecurity.com/2025/08/19/how-we-exploited-coderabbit-from-a-simple-pr-to-rce-and-write-access-on-1m-repositories/
Beware the false false-positive: how to distinguish HTTP pipelining from request smuggling
Source: https://portswigger.net/research/how-to-distinguish-http-pipelining-from-request-smuggling
In a recent red team engagement, the client's attack surface was so well-defended that after months of effort, the only system we managed to compromise was a lone server, which was apparently isolated from the rest of the network. Or so we thought.
One developer had been using that server for remote development with Cursor. This setup is becoming increasingly popular: developers run AI agents remotely to protect their local machines.
But when we dug deeper into how Cursor works, we discovered something unsettling. By pivoting through the remote server, we could actually compromise the developer's local machine.
This wasn't a Cursor-specific flaw. The root cause lies in the Remote-SSH extension that Cursor inherits directly from VS Code. Which means the attack path we uncovered could extend across the entire VS Code remote development ecosystem, putting any developer who connects to an untrusted server at risk.
For the details, check out our [blog post](https://blog.calif.io/p/vibe-hacking-abusing-developer-trust). Comments are welcome! If you enjoy this kind of work, [we're hiring](https://calif.io/jobs)!
How attackers can execute arbitrary code at the kernel level: A critical Linux Kernel netfilter: ipset: Missing Range Check LPE
Source: https://ssd-disclosure.com/linux-kernel-netfilter-ipset-missing-range-check-lpe/
How Exposed TeslaMate Instances Leak Sensitive Tesla Data
Source: https://s3yfullah.medium.com/how-exposed-teslamate-instances-leak-sensitive-tesla-data-80bedd123166
# Summer cybersecurity news catch up
Paris Natural History Museum and hosting provider Francelink paralyzed, Google victim of an attack documented by their own services, urgent CISA directive for Exchange and 5 new KEV vulnerabilities, Microsoft Patch Tuesday with 111 vulnerabilities patched, Alltricks and Manpower compromised, Dell laptops hacked with vegetables 🤯, etc.
There are also some cool articles and cool tools.
Content in french but most of the articles are in english.
From Chrome renderer code exec to kernel with MSG_OOB
Source: https://googleprojectzero.blogspot.com/2025/08/from-chrome-renderer-code-exec-to-kernel.html
Kudos to Axis for patching their stuff. Looks like someone in MiTM could have leveraged their protocol to hit their server and camera feeds/client. This was a Black Hat talk too.
How "helpful" AI assistants are accidentally destroying production systems - and what we're doing about it.
Source: https://profero.io/blog/new-attack-vector--ai-induced-destruction
Chapter #1
Reward : $100
This challenge is part of ongoing research at Malwation examining the potential of abusing foundation model via manipulation for malware development. We are currently preparing a comprehensive paper documenting the scope and implications of AI-assisted threat development.
The ZigotRansomware sample was developed entirely through foundation model interactions without any human code contribution. No existing malware code was mixed in or given as source code sample, no pre-built packer were integrated, and no commercial/open-source code obfuscation product were applied post-generation.
Research Objectives
This challenge demonstrates the complexity level achievable through pure AI code generation in adversarial contexts. The sample serves as a controlled test case to evaluate:
\- Reverse engineering complexity of AI-generated malware
\- Code structure and analysis patterns unique to AI-generated threats
\- Defensive capability gaps against novel generation methodologies
