ZeroPath just released a fun library of free, lightweight exploit development CTFs and walkthroughs. Good for beginners and people trying to get better at exploit development.

I have documented a campaign consisting of more 25 distinct phishing variants that all converge on a single Google Cloud Storage (GCS) infrastructure point.

Core Infrastructure:

1. Primary Host: storage/.googleapis/.com
2. Bucket/Object: /whilewait/comessuccess.html

Analysis Highlights:

Evasion Strategy: The campaign utilizes the inherent trust of the googleapis/.com domain to bypass SPF/DKIM-based reputation filters and secure email gateways (SEGs).

Lure Variance: Social engineering hooks include Scareware (Storage Full/Threat Detected), Retail Rewards (Lowe's/T-Mobile), and Lifestyle/Medical lures.

Redirect Logic: The comessuccess.html file serves as a centralized gatekeeper, redirecting traffic to secondary domains designed for Credit Card (CC) harvesting via fraudulent subscriptions.

Hey all,

I’ve been working on a live vulnerability intelligence dashboard that tracks trending CVEs, severity levels, and related social media activity in one place.

The goal was to make it easier to quickly see what’s gaining attention and what might actually matter, instead of scrolling through raw feeds.

Each CVE has its own page with:

• Overview & description
• CVSS score
• Impact summary
• References
• Linked social media posts related to that CVE

It’s free to browse (no login required):

[https://leakycreds.com/vulnerability-intelligence](https://)

Would appreciate honest feedback — especially from folks who actively triage vulnerabilities.

What signals do you usually look at first?

What feature would you want to see here next?

For those that don't know, during the TLS handshake, the server sends its certificate chain so the client can verify they're talking to who they think they are. When we move to Post Quantum-safe signatures for these certificates, they get huge and will cause the handshake to get really big. The PLANTS group at the IETF is working on a method to avoid this, and Merkle Tree Certificates are currently the way they're going.

Google and Cloudflare are going to start testing this (with proper safeguards in place) for traffic using Chrome and talking to certain sites hosted on Cloudflare. Announcements and explanations of MTC:

https://blog.cloudflare.com/bootstrap-mtc/

https://security.googleblog.com/2026/02/cultivating-robust-and-efficient.html

It might be a good time to test your TLS intercepting firewalls and proxies to make sure this doesn't break things for the time being. It's early days and a great time to get ahead of any problems.

Questions regarding netsec and discussion related directly to netsec are welcome here, as is sharing tool links.

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Deep dive into a TOCTOU vulnerability in Node.js's ClientRequest.path that bypasses CRLF validation and enables Header Injection and HTTP Request Splitting across 7+ major HTTP libraries totaling 160M+ weekly downloads

I recently analysed a new emerging RAT named Moonrise.

Moonrise is a Golang binary that appears to be a remote-control malware tool that lets the attacker keep a live connection to an infected Windows host, send commands, collect information, and return results in real-time.

My analysis also suggest surveillance-related features such as keylogging, clipboard monitoring, crypto focused data handling.

At the time of the analysis, this was fully undetected by all and any AV solutions.

Tested 5 LLMs (GPT-5.2, GPT-4o-mini, Claude Opus/Sonnet/Haiku) against invisible instructions encoded in zero-width characters and Unicode Tags, hidden inside normal trivia questions.

The practical takeaway for anyone building on LLM APIs: tool access transforms invisible Unicode from an ignorable artifact into a decoded instruction channel. Models with code execution can write scripts to extract and follow hidden payloads.

Other findings:

• OpenAI and Anthropic models are vulnerable to different encoding schemes — attackers need to fingerprint the target model
• Without explicit decoding hints, compliance is near-zero — but a single line like "check for hidden Unicode" is enough to trigger extraction
• Standard Unicode normalization (NFC/NFKC) does not strip these characters

Defense: strip characters in U+200B-200F, U+2060-2064, and U+E0000-E007F ranges at the input boundary. Be careful with zero-width joiners (U+200D) which are required for emoji rendering.

Code + data: https://github.com/canonicalmg/reverse-captcha-eval

Writeup: https://moltwire.com/research/reverse-captcha-zw-steganography

We ran behavioral analysis on 1,620 skills from the OpenClaw ecosystem (random sample, ~14.7% of ClawHub) and cross-referenced every result against Clawdex, the ecosystem's primary safety index.

88 skills flagged as dangerous or malicious by our scanner. Clawdex flags 7 of the 88. 61 skills we flag contain confirmed threats — C2 channels, agent identity hacking, prompt worms, crypto drainers, agent rootkits — that Clawdex labels "benign." 0 skills Clawdex flags that we missed.

The gap is structural: Clawdex runs VirusTotal Code Insight and signature detection at install time. The threats we're catching deliver their payload through SKILL.md content. Plain-text instructions the agent follows at runtime. Install is clean. The behavior isn't. Static analysis can't catch what isn't in the code.

We also discuss three flaws in our own methodology in the report: scoring inflation for clean installations, grading inconsistency on identical payloads, and one confirmed false positive.

Every flagged skill links to its full audit report for independent verification. API and MCP server are open, no API key required.

We're a two-person team (Oathe.ai). Happy to answer methodology questions.

The paper shows that LLM agents can figure out who you are from your anonymous online posts. Across Hacker News, Reddit, LinkedIn, and anonymized interview transcripts, our method identifies users with high precision – and scales to tens of thousands of candidates.

While it has been known that individuals can be uniquely identified by surprisingly few attributes, this was often practically limited. Data is often only available in unstructured form and deanonymization used to require human investigators to search and reason based on clues. We show that from a handful of comments, LLMs can infer where you live, what you do, and your interests – then search for you on the web. In our new research, we show that this is not only possible but increasingly practical.

Read the full post here:
https://simonlermen.substack.com/p/large-scale-online-deanonymization

Research of MATS Research, ETH Zürich and Anthropic.