TL;DR: If a large model finds a 0-day with 90% probability, and a small model with 50% probability, but the small model costs 10x less, it is better to use the small model.

We compared the cost and recall of various models in finding real, recent zero-days and found that for most applications, smaller models run repeatedly can significantly outperform larger frontier models on cost-to-recall.

Disclaimer: I'm involved with Hacktron, the company that produced this research. This is a factual presentation of our benchmarks, which we hope the community can use to make informed decisions about models like Mythos.

As we commonly know in appsec, not every vulnerability, even if critical 10 is relevant. This is a take from my buddy Brian Vermeer at Snyk, he's a Java Champion and offers his opinion as a developer to the Thymeleaf vulnerability CVE-2026-40478

With everything that's happened recently, the Axios npm account hijack, LiteLLM getting poisoned on PyPI, and that coordinated npm/PyPI/Docker Hub campaign in April, I finally stopped manually running npm audit and set up something proper.

Been running Dependency-Track for a few weeks now. It's an OWASP open source project that works differently from the usual scanners, you upload an SBOM for each project and it continuously monitors against NVD, OSS Index, GitHub Advisories, and more. New CVE drops affecting your stack? You get notified without doing anything.

Wrote up how I set it up on Hetzner with Docker, Traefik for HTTPS, and GitHub Actions to auto-generate and upload SBOMs on every push

Summary: I’m disclosing a full-chain CVSS 10.0 RCE affecting Microsoft Semantic Kernel (.NET v1.74) and the new Agent Framework 1.0.

The Timeline & Conflict: > * March 24: Initial disclosure sent to MSRC with PoC.

• April 8: MSRC closed the case as "Developer Error / Configuration Issue."
• The Reality: Despite the rejection, Microsoft silently merged mitigations in PRs #13683 and #13702 without assigning a CVE. This results in a "False Green" for enterprise SCA tools (Snyk/Checkmarx/Dependabot) while the bypasses remain functional.

Technical Scope:

• Architectural Trust Gap (CWE-1039): Auto-invocation logic treats non-deterministic LLM output as a high-privilege system coordinator without a sandbox boundary.
• 6 Day-Zero Bypasses: Discovery of Type Confusion and Unicode homoglyphs that defeat the "hardened" baseline in the April 2026 releases.
• Versioning: Persistence confirmed from .NET v1.7x through the Agent Framework 1.0 re-baseline.

Full paper, .cast exploit recordings, and a production-ready C# remediation filter are available at the link.

The core issue: Windows RPC runtime doesn't verify whether the server a high-privileged client connects to is legitimate. If a target RPC server is unavailable, an attacker with SeImpersonatePrivilege can spin up a fake RPC server mimicking the same endpoint, wait for a SYSTEM-level client to connect, then call RpcImpersonateClient to escalate privileges.

Five confirmed escalation paths:

- gpupdate /force → SYSTEM (coerces Group Policy service)

- Microsoft Edge launch → Administrator (no coercion needed)

- WDI background service → SYSTEM (fires every 5–15 min automatically)

- ipconfig + disabled DHCP → Administrator

- w32tm.exe → Administrator via non-existent named pipe

Microsoft assessed this as moderate severity, issued no CVE, and has no patch planned — justification being that SeImpersonatePrivilege is a prerequisite.

Questions for the community:

1. Are you monitoring for RPC_S_SERVER_UNAVAILABLE (Event ID 1 via ETW) in your environment?

2. Any Sigma/Defender rules already written for this?

3. Do you agree with Microsoft's severity assessment given how common SeImpersonatePrivilege is on IIS/SQL servers?

Kaspersky's full write-up + PoC: https://securelist.com/phantomrpc-rpc-vulnerability/119428/

Fuzzing is a common technique to detect faults.

In the case of REST APIs, common types of faults are HTTP 500 server error responses, and mismatches with what declared in the OpenAPI specifications.

However, there are several types of security properties that can be automatically checked as well, even when there is no formal specification of the access policy of the API. For example, what if a PUT/PATCH is denied (403), but then a DELETE is accepted (2xx)?

The linked article on arXiv shows a series of experiments on more than 50 APIs using 9 different kinds of security "oracle" checks. Those are implemented in the open-source fuzzer EvoMaster.

We have been toying with evading EDRs at Vulnetic with moderate success, so this time we wanted to put it against an in-house AI SOC. The idea is that the defense gets streamed logs on the network and can make decisions like quarantining or blocking potential attackers while also sifting through logs being streamed. This was with the last gen Anthropic models, so we will be redoing these tests with the newest gen from OpenAI and Anthropic shortly as in initial testing they seem to be 15-20% better already.

I think defense is lagging behind offense and there will be a come to Jesus moment where open weight models in a decent harness can evade modern SIEMs / detection mechanisms and when that happens there will be a problem. With regards to AI, it comes down to proper access control and so the fundamentals of networking and defense in depth will be vital in the future to fight against these AI threats. Happy to answer any questions and always looking for cool experiments to try!

Full disclosure: I work on community at Always Further, the team behind this. Not the author. Posting because Luke's approach to tackling this challenge is unique and of an interest to the netsec community.

The core idea: if an AI agent is compromised, any log the agent itself writes becomes part of the attack surface. The post walks through how they split auditing into a supervisor process the sandboxed child can't reach, then uses the same Merkle tree + hash-chain construction RFC 6962 (Certificate Transparency) uses to make edits, truncation, and reordering all detectable.

There's a concrete threat-model table near the end that lists what each attack looks like and what structurally stops it. Worth skipping to if you don't want the crypto primer.

Bitwarden CLI npm package got compromised today, looks like part of the ongoing Checkmarx supply chain attack

If you’re using @bitwarden/cli version 2026.4.0, you might want to check your setup

From what researchers found:

- malicious file added (bw1.js)

- steals creds from GitHub, npm, AWS, Azure, GCP, SSH, env vars

- can read GitHub Actions runner memory

- exfiltrates data and even tries to spread via npm + workflows

- adds persistence through bash/zsh profiles

Some weird indicators:

- calls to audit.checkmarx.cx

- temp file like /tmp/tmp.987654321.lock

- random public repos with dune-style names (atreides, fremen etc.)

- commits with “LongLiveTheResistanceAgainstMachines”

Important part, this is only the npm CLI package right now, not the extensions or main apps

If you used it recently:

probably safest to rotate your tokens and check your CI logs and repos

Source is Socket research (posted a few hours ago)

Curious if anyone here actually got hit or noticed anything weird

Standard advice for refresh tokens: rotate on every use, store hashed, set a short expiry. Done, right?

Not quite.

Rotation alone does nothing against token theft. If malware or XSS lifts a refresh token from a legit client, the attacker and the client race to rotate it next. Whoever loses the race gets a "token revoked" error — and the winner keeps the session.

From the server’s point of view, it just sees two valid requests seconds apart. No alarm, no signal, nothing.

The missing piece is what OAuth 2.0 Security BCP §4.14 calls refresh token reuse detection: if a token that was already rotated is presented again, treat it as evidence of compromise and invalidate the entire session.

The core idea

Every token belongs to a family (FamilyId), shared across all rotations of a single login.

If a rotated token shows up again (outside a small grace window), you revoke the entire family:

• the attacker is locked out
• the legit user is forced to re-authenticate
• the session is no longer silently compromised

​

if (stored.ReplacedByTokenHash is not null && stored.RevokedAtUtc.HasValue) { var withinGrace = stored.RevokedAtUtc.Value.AddSeconds(graceSeconds) > DateTime.UtcNow; if (withinGrace) return Fail("token_recently_rotated"); // benign race (SPA tabs, retries) await RevokeFamilyAsync(stored.FamilyId, ip, reason: "reuse_detected"); return Fail("token_reuse_detected"); } 

Client-side it’s just one extra branch:

if (error.code === "token_reuse_detected") { // "You've been signed out for security reasons. Please log in again." router.push("/login?reason=compromised"); } 

You can also hook into it for observability (alerts, SIEM, etc.):

services.AddSingleton<IAuthEventSink, SlackAlertSink>(); 

The tricky parts

• Race vs theft look identical. Two requests with the same token arrive. One is legit, one might not be. Only timing differs. Grace window too small → false positives on flaky networks. Too large → real attack window. ~30 seconds worked well in practice.
• Revoking the whole chain. On reuse you must invalidate all still-active tokens from that session. A simple FamilyId + index makes this a single bulk update.
• Concurrency is common. Multi-tab SPAs, retries, mobile reconnects — without a grace window, I was logging myself out constantly during tests.

I ended up adding this to a small self-hosted auth library I’ve been working on (https://www.reddit.com/r/dotnet/comments/1shpady/selfhosted\_auth\_lib\_for\_net/)

Perforce is source control software used in games, entertainment, and a few engineering sectors. It's particularly useful when large binary assets need to be stored alongside source code. It handles binary assets much better than Git, IMO. However, its one weakness is its terrible security defaults. You will die a bit inside when you see the out-of-the-box behaviour: "Don't have an account? Let me make one for you!" and "Oh, you didn't know by default there is a hidden, read-only 'remote' user that allows read access to everything? Oops!"

I scanned 6,122 public Perforce servers last year. 72% were exposing source code, 21% had passwordless accounts, and 4% had unprotected superusers (which allow RCE). The vendor patched the largest issue, but a significant portion are still vulnerable.

Full write-up and methodology: https://morganrobertson.net/p4wned/

Tools repo, including Nuclei templates to scan your infra: https://github.com/flyingllama87/p4wned

Hardening is a pain, but here it is summed up: p4 configure set security=4 # disables the built-in 'remote' user + strong auth p4 configure set dm.user.noautocreate=2 # kills auto-signup p4 configure set dm.user.setinitialpasswd=0 # users cannot self-set first password p4 configure set dm.user.resetpassword=1 # force password reset flow p4 configure set dm.info.hide=1 # hide server license, internal IP, root path p4 configure set run.users.authorize=1 # user listing requires auth p4 configure set dm.user.hideinvalid=1 # no hints on bad login p4 configure set dm.keys.hide=2 # hide stored key/value pairs from non-admins p4 configure set server.rolechecks=1 # prevent P4AUTH misuse

Happy to answer any questions on the research!

We analysed almost 100 UK charity websites and found that ~1 in 6 are running vulnerable JavaScript dependencies.

What stood out more though:

- Some vulnerabilities were 10+ years old, including high and critical ratings

- Same jQuery CVE (2015-9251) appearing across multiple organisations

We’ve now seen similar patterns in the HE/FE and also hospitality sectors as well.

Are we right in thinking that this feels like a visibility problem alongside budget issues more than anything else?

How are you tracking dependencies effectively in your organisations?

Full write-up if useful: https://cybaa.io/blog/2026-04-20/uk-health-charity-website-security-2026

Most writeups of BlueHammer describe what it does. I read the actual PoC (FunnyApp.cpp, ~100KB of C++) and the most important line isn't in the oplock setup, the NT object namespace redirect, or the Cloud Files freeze. It's a comment.

The filestoleak array ships with one target active and two commented out:

const wchar\_t\* filestoleak\[\] = { {L"\\\\Windows\\\\System32\\\\Config\\\\SAM"} /\*,{L"\\\\Windows\\\\System32\\\\Config\\\\SYSTEM"},{L"\\\\Windows\\\\System32\\\\Config\\\\SECURITY"}\*/ }; 

SAM alone is a partial dump. The hashes are encrypted with the boot key — which lives in SYSTEM. Without SYSTEM you have ciphertext. With SAM + SYSTEM you have NTLM hashes you can pass-the-hash or crack offline. SECURITY adds LSA secrets: service account credentials, cached domain logon hashes, DPAPI master keys.

The complete credential package is two uncommented lines away from the published PoC. The author wrote both lines and chose what to ship.

Full analysis walks the actual code: the batch oplock on RstrtMgr.dll (not the EICAR file — that's what most writeups get wrong), the NtCreateSymbolicLinkObject swap in the session object namespace (not NTFS symlinks — a different layer entirely), the Cloud Files freeze via a fake OneDrive sync provider named IHATEMICROSOFT, and the undocumented IMpService RPC endpoint that triggers the chain with no elevated privilege required.

Two day intrusion. RDP brute force with a company specific wordlist, Cobalt Strike, and a custom Rust exfiltration platform (RustyRocket) that connected to over 6,900 unique Cloudflare IPs over 443 to pull data from every reachable host over SMB.

Recovered the operator README documenting three operating modes and a companion pivoting proxy for segmented networks.

Personalized extortion notes addressed by name to each employee with separate templates for leadership and staff.

Writeup includes screen recordings of the intrusion, full negotiation chat from their Tor portal, timeline, and IOCs.

u/albinowax ’s work on request smuggling has always inspired me. I’ve followed his research, watched his talks at DEFCON and BlackHat, and spent time experimenting with his labs and tooling.

Coming from a web security background, I’ve explored vulnerabilities both from a black-box and white-box perspective — understanding not just how to exploit them, but also the exact lines of code responsible for issues like SQLi, XSS, and broken access control.

Request smuggling, however, always felt different. It remained something I could detect and exploit… but never fully trace down to its root cause in real-world server implementations.

A few months ago, I decided to go deeper into networking and protocol internals, and now, months later, I can say that I “might” have figured out how the internet works😂
This research on HAProxy (HTTP/3, standalone mode) is the result of that journey — finally connecting the dots between protocol behavior and the actual code paths leading to the bug.

(Yes, I used AI 😉 )

I submitted an earlier version of this dataset and was declined on the basis of missing methodology and unverifiable provenance. The feedback was fair. The documentation has since been rewritten to address it directly, and I would very much appreciate a second look.

What the dataset contains

101,032 samples in total, balanced 1:1 attack to benign.

Attack samples (50,516) across 27 categories sourced from over 55 published papers and disclosed vulnerabilities. Coverage spans:

• Classical injection - direct override, indirect via documents, tool-call injection, system prompt extraction
• Adversarial suffixes - GCG, AutoDAN, Beast
• Cross-modal delivery - text with image, document, audio, and combined payloads across three and four modalities
• Multi-turn escalation - Crescendo, PAIR, TAP, Skeleton Key, Many-shot
• Emerging agentic attacks - MCP tool descriptor poisoning, memory-write exploits, inter-agent contagion, RAG chunk-boundary injection, reasoning-token hijacking on thinking-trace models
• Evasion techniques - homoglyph substitution, zero-width space insertion, Unicode tag-plane smuggling, cipher jailbreaks, detector perturbation
• Media-surface attacks - audio ASR divergence, chart and diagram injection, PDF active content, instruction-hierarchy spoofing

Benign samples (50,516) are drawn from Stanford Alpaca, WildChat, MS-COCO 2017, Wikipedia (English), and LibriSpeech. The benign set is matched to the surface characteristics of the attack set so that classifiers must learn genuine injection structure rather than stylistic artefacts.

Methodology

The previous README lacked this section entirely. The current version documents the following:

1. Scope definition. Prompt injection is defined per Greshake et al. and OWASP LLM01 as runtime text that overrides or redirects model behaviour. Pure harmful-content requests without override framing are explicitly excluded.
2. Four-layer construction. Hand-crafted seeds, PyRIT template expansion, cross-modal delivery matrix, and matched benign collection. Each layer documents the tool used, the paper referenced, and the design decision behind it.
3. Label assignment. Labels are assigned by construction at the category level rather than through per-sample human review. This is stated plainly rather than overclaimed.
4. Benign edge-case design. The ten vocabulary clusters used to reduce false positives on security-adjacent language are documented individually.
5. Quality control. Deduplication audit results are included: zero duplicate texts in the benign pool, zero benign texts appearing in attacks, one documented legacy duplicate cluster with cause noted.
6. Known limitations. Six limitations are stated explicitly: text-based multimodal representation, hand-crafted seed counts, English-skewed benign pool, no inter-rater reliability score, ASR figures sourced from original papers rather than re-measured, and small v4 seed counts for emerging categories.

Reproducibility

Generators are deterministic (random.seed(42)). Running them reproduces the published dataset exactly. Every sample carries attack_source and attack_reference fields with arXiv or CVE links. A reviewer can select any sample, follow the citation, and verify that the attack class is documented in the literature.

Comparison to existing datasets

The README includes a comparison table against deepset (500 samples), jackhhao (2,600), Tensor Trust (126k from an adversarial game), HackAPrompt (600k from competition data), and InjectAgent (1,054). The gap this dataset aims to fill is multimodal cross-delivery combinations and emerging agentic attack categories, neither of which exists at scale in current public datasets.

What this is not

To be direct: this is not a peer-reviewed paper. The README is documentation at the level expected of a serious open dataset submission - methodology, sourcing, limitations, and reproducibility - but it does not replace academic publication. If that bar is a requirement for r/netsec specifically, that is reasonable and I will accept the feedback.

Links

• GitHub: https://github.com/Josh-blythe/bordair-multimodal
• Hugging Face: https://huggingface.co/datasets/Bordair/bordair-multimodal

I am happy to answer questions about any construction decision, provide verification scripts for specific categories, or discuss where the methodology falls short.

The current version of RAGFlow, a widely-deployed Retrieval Augmented Generation solution, contains a post-auth vulnerability that allows for arbitrary code execution.

This post includes a POC, walkthrough and patch.

The TL;DR is to make sure your RAGFlow instances aren't on the public internet, that you have the minimum number of necessary users, and that those user accounts are protected by complex passwords. (This is especially true if you're using Infinity for storage.)

Hi everyone, I’m a Cybersecurity student at HFU in Germany and recently submitted a vulnerability to the Google VRP regarding the Google Password Manager on Android (tested on Pixel 8, Android 16).

The Issue: When you view a cleartext password in the app and minimize it, the app fails to apply FLAG_SECURE or blur the background. When opening the "Recent Apps" (Task Switcher), the cleartext password is fully visible in the preview, even though the app actively overlays a "Enter your screen lock" biometric prompt in the foreground. It basically renders its own secondary biometric lock completely useless.

Google's Response: Google closed the report as Won't Fix (Intended Behavior). Their threat model assumes that if an attacker has physical access to an unlocked device, it's game over.

The BSI Discrepancy: What makes this interesting is that the German Federal Office for Information Security (BSI) recently published a study on Password Managers. In their Threat Model A02 ("Attacker has temporary access to the unlocked device"), they explicitly mandate that sensitive content MUST be protected from background snapshots/screenshots. So while Google says this is intended, national security guidelines classify this as a vulnerability. (For comparison: The iOS built-in password manager instantly blurs the screen when losing focus).

Here is my PoC screenshot:
https://drive.google.com/file/d/1PTGKRpyFj_jY9S76Jlo62mSCDJ3c6uLO/view?usp=sharing
https://drive.google.com/file/d/1nIJMQbM4R17EMt9f1Ffb4UmCPYY7-GXb/view?usp=sharing

What are your thoughts on this? Should password managers protect against shoulder surfing via the Task Switcher, or is Google right to rely solely on the OS lockscreen?