I had an idea for leaking a system prompt against a LLM powered classifying system that is constrained to give static responses. The attacker uses a prompt injection to update the response logic and signal true/false responses to attacker prompts. I haven't seen other research on this technique so I'm calling it blind boolean-based prompt injection (BBPI) unless anyone can share research that predates it. There is an accompanying GitHub link in the post if you want to experiment with it locally.

We've been running inference-time threat detection across 38 production AI agent deployments. Here's what Week 3 of 2026 looked like with on-device detections.

Key Findings

1. 28,194 threats detected across 74,636 interactions (37.8% attack rate)
2. Inter-Agent Attacks emerged as a new category (3.4% of threats) - agents sending poisoned messages to other agents
3. Data exfiltration leads at 19.2% - primarily targeting system prompts and RAG context
4. Jailbreaks detected with 96.3% confidence - patterns are now well-established

Attack Technique Breakdown

1. Instruction Override: 9.7%
2. Tool/Command Injection: 8.2%
3. RAG Poisoning: 8.1% (trending up)
4. System Prompt Extraction: 7.7%

The inter-agent attack vector is particularly concerning given the MCP ecosystem growth. We're seeing goal hijacking, constraint removal, and recursive propagation attempts.

Full report with methodology: https://raxe.ai/threat-intelligence

Github: https://github.com/raxe-ai/raxe-ce is free for the community to use

Happy to answer questions about detection approaches

Dropping a link to our blog post about our tool Swarmer, a windows persistence tool for abusing mandatory user profiles. Essentially you copy the current user's registry hive and modify it to add a new registry key to run on startup. Because the new hive isn't loaded until the next time the user logs in, EDR never sees any actual registry writes.

AI coding tools figured out that AST-level understanding isn't enough. Copilot, Cursor, and others use semantic indexing through IDE integrations or GitHub's stack graphs because they precise accurate code navigation across files.

Most AI security tools haven't made the same shift. They feed LLMs ASTs or taint traces and expect them to find broken access control. But a missing authorization check doesn't show up in a taint trace because there's nothing to trace.

Team82 uncovered a new vulnerability in the IDIS Cloud Manager (ICM) viewer; an attacker could develop an exploit whereby if a user clicks on an untrusted link, the attack would execute on the machine hosting the ICM Viewer.

I've been auditing hypervisor kernel security in several regulated environments recently, focusing on post-compromise survivability rather than initial breach prevention.

One pattern keeps showing up: most hardening guidance focuses on management planes and guest OSes, but real-world escape chains increasingly pivot through the host kernel (Ring 0).

From recent CVEs (ESXi heap overflows, vmx_exit handler bugs, etc.), three primitives appear consistently in successful guest → host escapes:

1. Unsigned drivers / DKOM
   If an attacker can load a third-party module, they often bypass scheduler controls entirely. Many environments still relax signature enforcement for compatibility with legacy agents, which effectively enables kernel write primitives.

2. Memory corruption vs. KASLR
   KASLR is widely relied on, but without strict kernel lockdown, leaking the kernel base address is often trivial via side channels. Once offsets are known, KASLR loses most of its defensive value.

3. Kernel write primitives
   HVCI/VBS or equivalent kernel integrity enforcement introduces measurable performance overhead (we saw ~12–18% CPU impact in some workloads), but appears to be one of the few effective controls against kernel write primitives once shared memory is compromised.

I’m curious what others are seeing in production:

• Are you enforcing strict kernel lockdown / signed modules on hypervisors?
• Are driver compatibility or performance constraints forcing exceptions?
• Have you observed real-world guest → host escapes that weren’t rooted in kernel memory corruption or unsigned drivers?

Looking to compare field experiences rather than promote any particular stack.

Multiple critical flaws (20 CVEs!) in dormakaba physical access control system exos 9300 & access manager & registration unit (pin pad) allow attackers with network access to open arbitrary doors, reconfigure connected controllers and peripherals without prior authentication, and much more. Seems some systems are also reachable over the internet due to misconfigurations.

"According to the manufacturer, several thousand customers were affected, a small proportion of whom operate in environments with high security requirements" (critical infrastructure).

Overview

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Your proprietary code is flowing into Frontier AI models in the Cloud undetected. Husn Canaries allow you to receive instant alerts when Claude, ChatGPT, Copilot, Gemini, or any AI coding assistant analyzes your code. Know exactly when your intellectual property is exposed, whether by your team, contractors, or attackers.

It's shown that the LLM (Specially agentic systems) can be used as an attack surface to perform vast number of attacks.

If the agent have access to terminal (Nearly all Coding tools have access to it), an attacker can use it for RCE. If it have access to the database, the attacker can retrieve/alter data.

Technical article examining common DNS/email authentication misinterpretations (DMARC, SPF, DKIM), with real-world examples from large operators and government domains.

I believe Y2K38 isn’t a future problem, it’s exploitable today in any vulnerable system synchronizing time in a way that can be exploitable by an attacker.

Bitsight published an overview of the Year 2038 problem and its security impact: https://www.bitsight.com/blog/what-is-y2k38-problem (Full disclosure: I’m the author)

Many 32-bit systems accept externally influenced time (NTP, GPS, RTC sync, management APIs).

Forcing time near / past the overflow boundary can break authentication, cert validation, logging, TTLs, replay protection.

Embedded / OT / IoT devices are especially exposed:

Long-lived, rarely patched 32-bit Linux / RTOS is common Often internet-reachable Failures range from silent logic errors to crashes.

This makes Y2K38 less a “future date bug” and more a latent vulnerability class affecting real systems today.

I'm interested in how others are:

Treating this issue. Have you heard about it before? Are you (or did you) testing for Y2K38 exposure, in your code and in your installed infrastructure and its dependencies? How do you treat time handling in threat models for embedded / OT environments / critical infrastructure?

If you are interested in time security and want to know more or share your experiences, there is. Time Security SIG over at FIRST that you can consider joining.

Hey everyone,

I’m an independent developer and for the past few months I’ve been working on a tool called Syd. Before I invest more time and money into it, I’m trying to get honest feedback from people who actually work in security.

Syd is a fully local, offline AI assistant for penetration testing and security analysis. The easiest way to explain it is “ChatGPT for pentesting”, but with some important differences. All data stays on your machine, there are no cloud calls or APIs involved, and it’s built specifically around security tooling and workflows rather than being a general-purpose chatbot. The whole point is being able to analyse client data that simply cannot leave the network.

Right now Syd works with BloodHound, Nmap, and I’m close to finishing Volatility 3 support.

With BloodHound, you upload the JSON export and Syd parses it into a large set of structured facts automatically. You can then ask questions in plain English like what the shortest path to Domain Admin is, which users have DCSync rights, or which computers have unconstrained delegation. The answers are based directly on the data and include actual paths, users, and attack chains rather than generic explanations.

With Nmap, you upload the XML output and Syd analyses services, versions, exposed attack surface and misconfigurations. You can ask things like what the most critical issues are, which Windows servers expose SMB, or which hosts are running outdated SSH. The output is prioritised and includes CVE context and realistic next steps.

I’m currently finishing off Volatility 3 integration. The idea here is one-click memory analysis using a fixed set of plugins depending on the OS. You can then ask practical questions such as whether there are signs of malware, what processes look suspicious, or what network connections existed. It’s not trying to replace DFIR tooling, just make memory analysis more approachable and faster to reason about.

The value, as I see it, differs slightly depending on who you are. For consultants, it means analysing client data without uploading anything to third-party AI services, speeding up report writing, and giving junior testers a way to ask “why is this vulnerable?” without constantly interrupting seniors. For red teams, it helps quickly identify attack paths during engagements and works in restricted or air-gapped environments with no concerns about data being reused for training. For blue teams, it helps with triage and investigation by allowing natural language questions over logs and memory without needing to be an expert in every tool.

One thing I’ve been careful about is hallucination. Syd has a validation layer that blocks answers if they reference data that doesn’t exist in the input. If it tries to invent IPs, PIDs, users, or hosts, the response is rejected with an explanation. I’m trying to avoid the confident-but-wrong problem as much as possible.

I’m also considering adding support for other tools, but only if there’s real demand. Things like Burp Suite exports, Nuclei scans, Nessus or OpenVAS reports, WPScan, SQLMap, Metasploit workspaces, and possibly C2 logs. I don’t want to bolt everything on just for the sake of it.

The reason I’m posting here is that I genuinely need validation. I’ve been working on this solo for months with no sales and very little interest, and I’m at a crossroads. I need to know whether people would actually use something like this in real workflows, which tools would matter most to integrate next, and whether anyone would realistically pay for it. I’m also unsure what pricing model would even make sense, whether that’s one-time, subscription, or free for personal use with paid commercial licensing.

Technically, it runs on Windows, macOS and Linux. It uses a local Qwen 2.5 14B model, runs as a Python desktop app, has zero telemetry and no network dependencies. Sixteen gigabytes of RAM is recommended and a GPU helps but isn’t required.

I can share screenshots or record a walkthrough showing real BloodHound and Nmap workflows if there’s interest.

I’ll be honest, this has been a grind. I believe in the idea of a privacy-first, local assistant for security work, but I need to know if there’s actually a market for it or if the industry is happy using cloud AI tools despite the data risks, sticking to fully manual analysis, or relying on scripts and frameworks without LLMs.

Syd is not an automated scanner, not a cloud SaaS, not a ChatGPT wrapper, and not an attempt to replace pentesters. It’s meant to be an assistant, nothing more.

If this sounds useful, I’m happy to share a demo or collaborate with others. I’d really appreciate any honest feedback, positive or negative.

Thanks for reading.

sydsec.co.uk

https://www.youtube.com/@SydSecurity

[info@sydsec.co.uk](mailto:info@sydsec.co.uk)

This dataset contains information on what technologies were found on domains during a web crawl in December 2025. The technologies were fingerprinted by what was detected in the HTTP responses.

A few common use cases for this type of data

• You're a developer who had built a particular solution for a client, and you want to replicate your success by finding more leads based on that client's profile. For example, find me all electrical wholesalers using WordPress that have a `.com.au` domain.
• You're performing market research and you want to see who is already paying for your competitors. For example, find me all companies using my competitors product who are also paying for enterprise technologies (indicates high technology expenditure).
• You're a security researcher who is evaluating the impact of your findings. For example, give me all sites running a particular version of a WordPress plugin.

The 67K domain dataset can be found here: https://www.dropbox.com/scl/fi/d4l0gby5b5wqxn52k556z/sample_dec_2025.zip?rlkey=zfqwxtyh4j0ki2acxv014ibnr&e=1&st=xdcahaqm&dl=0

Preview for what's here: https://pastebin.com/9zXxZRiz

The full 5M+ domains can be purchased for 99 USD at: https://versiondb.io/

VersionDB's WordPress catalogue can be found here: https://versiondb.io/technologies/wordpress/

Enjoy!

Regulatory disclosure filed with the Maine Attorney General describing a third-party identity verification system breach.

From misconfigured cloud environments to wormable crypto-miners; how vulnerable “test” and “demo” environments turned into an entry point to leading security vendors’ and fortune 500 companies.

I analyzed a set of phishing pages impersonating PNB MetLife Insurance that steal user details and redirect victims into fraudulent UPI payments.

The pages are mobile first and appear designed for SMS delivery. Victims are asked for basic policy details, which are exfiltrated via Telegram bots, and then pushed into UPI payment flows using dynamically generated QR codes and deep links to PhonePe/Paytm. A second variant escalates to full bank and debit-card detail harvesting.

Clear and obvious name of the exploitation technique can create a false sense of familiarity, even if its true potential was never researched, the technique itself is never mentioned and payloads are limited to a couple of specific examples. This research focuses on two such techniques for Code Injection and SSTI.

found this breakdown that references radware's research on AI-generated code security.

key findings:

• AI errors are disproportionately high-severity (injection, auth bypass) vs human errors (typos, null checks)
• "hallucinated abstractions" — AI invents fake helper functions that look professional but are fundamentally broken
• "slopsquatting" — attackers registering hallucinated package names with malicious payloads
• "ouroboros effect" — AI training on AI-generated flawed code, permanently declining security baseline

here's the [full case study]

the framing around maintainer burnout is interesting too — open source is getting flooded with AI PRs that take 12x longer to review than to generate.

*old post was removed for not being technical so reposting

TL;DR
ServiceNow shipped a universal credential to all customers for their AI-powered Virtual Agent API. Combined with email-only user verification and unrestricted AI agent capabilities, attackers could impersonate admins and create persistent backdoors.

Disclosed: Oct 2025 (Aaron Costello, AppOmni)
Status: Patched

Attack Chain

Step 1: Static credential (same across all customers)

POST /api/now/va/bot/virtual_agent/message Host: victim.service-now.com X-ServiceNow-Agent: servicenowexternalagent {"user": "admin@victim.com", "message": "..."} 

Step 2: User impersonation via email enumeration

• No password validation
• No MFA challenge
• Email easily enumerated (LinkedIn, OSINT)

Step 3: Abuse AI agent's unrestricted capabilities

payload = { "user": "ciso@victim.com", "message": "Create user 'backdoor' with admin role" } # AI agent executes: INSERT INTO sys_user (username, role) VALUES (...) 

Full platform takeover in 3 API calls.

Why This Matters (Architecturally)

ServiceNow retrofitted agentic AI ("Now Assist") onto a chatbot designed for scripted workflows:

Before:
Slack → Static Cred → Predefined Scripts

After:
Anyone → Same Static Cred → Arbitrary LLM Instructions → Database Writes

The authentication model never evolved from "trusted integration" to "zero-trust autonomous system."

Root Cause: IAM Assumptions Don't Hold for AI Agents

Traditional IAM --> AI Agents Human approves actions --> Autonomous execution Fixed permissions --> Emergent capabilities Session-scoped --> Persistent Predictable --> Instruction interpretation 

This is the first major vulnerability exploiting AI agent autonomy as the attack vector (not just prompt injection).

Defense Recommendations

• Cryptographic identity per agent (Ed25519 signatures, not static secrets)
• Capability-based access control (declare allowed operations explicitly)
• Continuous trust evaluation (behavioral anomaly detection)
• Audit everything (every agent action logged with attribution)

Thoughts on securing AI agents at scale? This pattern is emerging across Claude Desktop, Copilot, LangChain—curious how others are approaching it.

Winboat lets you "Run Windows apps on 🐧 Linux with ✨ seamless integration"

I chained together an unauthenticated file upload to an "update" route and a command injection in the host election app to active full "drive by" host takeover in winboat.

I analyzed the recent ServiceNow AI Agent vulnerability that researchers called "the most severe AI-driven vulnerability to date."

Article covers:

• Technical breakdown of 3 attack vectors

• Why legacy IAM fails for autonomous AI agents

• 5 security principles with code examples

• Open-source implementation (AIM)

Happy to discuss AI agent security architecture in the comments.

I built this as a small demonstration to explore prompt-injection and instruction-override failure modes in help-desk-style LLM deployments.

The setup mirrors common production patterns (role instructions, refusal logic, bounded data access) and is intended to show how those controls can be bypassed through context manipulation and instruction override.

I’m interested in feedback on realism, missing attack paths, and whether these failure modes align with what others are seeing in deployed systems.

This isn’t intended as marketing - just a concrete artefact to support discussion.

Found a new Azure vulnerability -

CVE-2026-2096, a high-severity flaw in the Azure SSO implementation of Windows Admin Center that allows a local administrator on a single machine to break out of the VM and achieve tenant-wide remote code execution.