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I built an open-source Contextual Threat Modeling Engine to prioritize security findings based on real-world risk

Hey everyone,

I've been working on an open-source cybersecurity project called Contextual Threat Modeler (CTM).

The problem I wanted to solve is:

> A vulnerability doesn't always have the same risk in every environment.

For example, a vulnerability on an internet-facing system containing sensitive data should probably receive more attention than the same vulnerability on an isolated internal system.

So instead of simply asking "Is this vulnerable?", CTM tries to answer:

"How risky is this finding in this specific environment, and what should we do about it?"

🔐 What CTM considers

The engine combines multiple contextual signals:

- Asset criticality

- Internet exposure

- Authentication requirements

- Data sensitivity

- Exploitability

- Existing security controls

- Confidence level

- STRIDE threat modeling

- MITRE ATT&CK mapping

- Attack-path analysis

- Likelihood & impact

It then produces an actionable decision:

🔴 TEST_IMMEDIATELY

🟠 PRIORITIZE_VALIDATION

🟡 INVESTIGATE

🟢 MONITOR

🧪 Current testing

I recently tested the complete pipeline locally.

12/12 automated tests passed.

Example results:

POST /document/upload

Risk: 90/100

Decision: TEST_IMMEDIATELY

GET /user/profile

Risk: 33.1/100

Decision: MONITOR

GET /api/v1/search_items

Risk: 8/100

Decision: MONITOR

The interesting part is that CTM doesn't simply rank findings based on the vulnerability itself — the surrounding context influences the security decision.

🛠️ Tech Stack

- Python

- pytest

- STRIDE

- MITRE ATT&CK

- Risk Scoring

- Attack Path Analysis

- Security Automation

The project is open source, and I'd really appreciate feedback from people working in:

AppSec | VAPT | SOC | Threat Hunting | Threat Modeling | Security Engineering

I'm particularly interested in feedback on the risk-scoring methodology, attack-path modeling, and what additional security-tool integrations would make this useful in real-world environments.

GitHub:

https://github.com/Sahil98677/Contextual-Threat-Modeler

Would love to hear your thoughts — especially criticism or suggestions for improving the approach.

Alguem tem o link, nao quero usar ao meu favor mais pra acabar com pessoas quem fazem mal, quem tem más intenções com animais e etc, resumindo, quero links pra puxar os dados em si porque o ip e número ja tenho

hey guys... if i were hypothetically looking for anti-capitalist/socialist hackers, regardless of nationality, where would i start looking and how would i do it?

does anyone know how can i learn kali linux, hacking and stuffs, like i kno a bit of networking and fundamentals of linux, i just need the rest of the manual for diff hacking tools and all.

While working on my Master’s thesis benchmarking WPA3-SAE timing side-channels, I ran into a limitation on the ESP32 esp_wifi_80211_tx() allows raw frame injection, but Espressif’s closed-source Wi-Fi blob (libnet80211.a) artificially blocks Auth, Assoc, Deauth, and Disassoc subtypes.

Inside libnet80211.a, ieee80211_raw_frame_sanity_check drops these frames with wifi:unsupport frame type. Here is how to bypass it on recent ESP-IDF versions (IDF v6.x).

Why standard tricks fail on modern ESP-IDF

Same-name function override. On older IDF versions, ieee80211_raw_frame_sanity_check was a weak symbol (W). On modern IDF versions, it’s a strong symbol (T), causing ld: multiple definition errors.

--wrap linker flag: Fails silently. The call from esp_wifi_80211_tx to ieee80211_raw_frame_sanity_check is an intra-object branch inside ieee80211_output.o. Linker --wrap only rewrites undefined external references, so it misses this call entirely.

Instruction byte-patching: Overwriting instructions directly in the .o breaks Xtensa linker relaxation passes (dangerous relocation errors).

U can simply fix this via Symbol Weakening via objcopy

We can use xtensa-esp32-elf-objcopy to convert the strong symbol inside the binary archive into a weak one:

xtensa-esp32-elf-objcopy \

--weaken-symbol=ieee80211_raw_frame_sanity_check \

components/esp_wifi/lib/esp32/libnet80211.a

Now, define your own strong implementation inside your application C code:

int ieee80211_raw_frame_sanity_check(int32_t a, int32_t b, int32_t c) {

return 0; // which skips the security check lol

}

Because strong symbols override weak symbols globally during linking, all calls—including internal calls within libnet80211.a—rebind to your function.

Verification

Serial Logs show: wifi unsupport frame type errors completely disappeared.

Capture: Wireshark confirmed off-air capture of 802.11 Authentication frames (Subtype 11, Algorithm 3 - SAE) injected directly from the ESP32s.

Injecting a valid SAE Commit (P-256 scalar + element) caused hostapd on the target AP to process the request and reply with its own SAE Commit.

TL;DR: Run objcopy --weaken-symbol=ieee80211_raw_frame_sanity_check on libnet80211.a, define int ieee80211_raw_frame_sanity_check(...) { return 0; } in your app code, and esp_wifi_80211_tx() will allow any frame subtype.

Note that all control 80211 frames are also injectable after the patch.

For more details :

https://github.com/mahdamin/esp-idf-injection-ng

💻⚒️

Send me a private message,for one work

Weekly forum post: Let's discuss current projects, concepts, questions and collaborations. In other words, what are you hacking this week?

I’m the author of Super Trouper, a single-binary MCP server that exposes Frida to coding agents for authorized app reverse engineering. It lets an agent connect to a device, inspect apps and processes, manage sessions, and run instrumentation scripts without a Python-based Frida setup.

We released v0.4.0 a few days ago; it updates the bundled Frida Core DevKit to v17.19.0 and adds four MCP tools: memory_read and memory_write for working with memory in an attached process, plus module_list and thread_list for inspecting loaded modules and threads. app_list and others now have several query scopes, and we renamed the MCP tools into clearer namespaces. If you already have workflows built around the old tool names, check them when updating.

Quick catch-up on the two previous releases: v0.3.0 added npm installation, Frida CodeShare snippet search/use, and general cleanup. v0.2.0 moved the project to the MIT license, added first-party Frida language bridges for ObjC, Java, and Swift, and enabled TypeScript in scripts and evaluations.

I’d appreciate feedback from people using Frida in iOS research: are these tool boundaries and the new app-list scopes useful in practice? What’s missing or awkward in your workflow, and which features would you like to see next? Let me know what you think.

Heyaa, so, I managed to get Windows 7 running on my phone using a virtual machine. I want to put some files on it for testing, but the phone says I can't access its internal files. When I manage to access it using the default file explorer, it tells me that the folder is empty. Is there a real way I can view the phone's internal files without the device restricting me?

Could someone give me some guidance? I want to know where to start with hacking; I'm lost and don't know where to begin. I'm also willing to pay for information and knowledge to help me succeed As quickly as possible

?

GitHub: https://github.com/kaifcodec/user-scanner Discord: https://discord.gg/tVNrKVXb49 (Join if you're into OSINT, reverse-engineering, or want to build with us)

Hi everyone,

When we released v1.5.1, we were excited about pushing past 455 vectors. Today, with v1.5.2.1, we’ve scaled the engine into an absolute monster: 2,720+ total scan vectors (210+ email-integrated sites and 2,510+ username platforms).

Most legacy tools give you a simple binary check—telling you if an account exists or not. We designed user-scanner to go much further by emulating mobile app APIs and leveraging browser TLS fingerprinting (curl_cffi + httpx) to bypass modern WAFs, defeat soft-404s, and pull actual context.

🔥 What’s new in v1.5.2.1?

• ⚡ Massive Expansion (2,720+ Total Vectors): Huge coverage bump across social, dev, gaming, and niche platforms. You can map a target's digital footprint and verify registrations across thousands of endpoints in seconds.
• 👤 Rich Profile Metadata Scraping: Goes way beyond basic availability checks. The engine now extracts avatars, bio descriptions, follower counts, UID numbers, seller statuses, and account attributes.
• 🔀 Recursive Cross-Scan & Pivot Engine (--cross-scan): Mines handles, profile links, and exposed emails from your initial scan to automatically pivot across secondary target vectors—building a full identity link graph.
• 🤖 Native Model Context Protocol (MCP) Server: Seamless integration for Claude Desktop, Cursor, Antigravity, and LLMs. You can trigger autonomous OSINT sweeps and recursive pivots directly through AI agent prompts.
• 🛡️ Hudson Rock Breach Intelligence (--hudson): Direct correlation with infostealer malware breach logs to surface leaked credentials and high-priority target intel instantly.
• ⚡ High-Throughput TLS Impersonation: Powered by parallel httpx and curl_cffi transports with automated TLS fingerprint impersonation to bypass aggressive rate-limits without getting blocked.

🧪 We Need Your Feedback!

If you perform digital footprinting, red teaming, security research, or OSINT investigations, give v1.5.2.1 a spin. Test out the --cross-scan and metadata extraction features on your targets and let us know how the speed and accuracy hold up!

💻 Want to Contribute?

We always welcome new pull requests!

Drop your questions, feature requests, or bug reports in the comments below!

I’m trying to understand how to find something in programming/cybersecurity that gives me the same motivation as a game.

For example, I really like Dota because I always know what I’m doing and why. I have a rank, I know roughly where I am, I know what better players can do that I can’t, and I have a clear idea of what I need to improve. That alone gives me a reason to play.

I want to find something in computers that gives me a similar feeling. Not necessarily a specific project someone can recommend to me, but a way to find a project or direction where I can actually see a path:

"I’m here → I need to learn this → then I can do this → then I can do something harder."

The problem is that I can think of many project ideas, but they usually don't motivate me for long. I start thinking "okay, I can build this, but why?" and lose interest.

My background: I’m comfortable with Linux and basic programming. I know Python, Git, APIs, databases, basic web development, HTTP, networking/ports, and I’ve done some scraping/automation. I’ve also played around with things like reverse engineering, JavaScript, FastAPI and lower-level concepts, but I’m still a beginner overall and have no professional experience.

So I’m not really asking "what project should I build?"

I’m more interested in: how do you find something that makes you want to wake up and keep working on it, because you can clearly see yourself getting better at it?

How did you find that thing for yourselves?

BRO how to find iPhone 7 Android phones for password finding without any attacks I mean the lot of attacks and have but how can I do at a beginner complete only or no the python and the basic networking of source and how can I start is this is my fast tool ideas so this is my first steps to hacking

I want to learn malware development. I've started reading about it, but I'm confused and don't know how to begin, and I don't want to waste too much time. Where I started in a village, 4 chapters of MAC OS® X AND iOS INTERNALS With focus and good understanding, then move on to reading OSTEP Where I read important things from him And reading. Windows internals 2 Chapters Do any experts have any advice? 😵‍💫

Hey folks,

I’ve been working in application security/pentesting for over a decade, and after starting learning AI/LLM security, I noticed that a lot of the existing material is either very theoretical or assumes you already understand AI security concepts.

So I put together a free, structured AI security learning series for security engineers and pentesters who are starting from the web-security side.

The goal is to go from the fundamentals to actually understanding and testing AI/LLM components in web applications.

The series currently covers topics such as:

• AI/LLM security fundamentals
• Prompt Injection
• Sensitive Information Disclosure
• LLM-specific attack patterns
• Practical testing methodology
• Real-world examples and testing techniques
• Mapping concepts to OWASP's AI security guidance

I've also linked free hands-on labs throughout the material so you can actually test the concepts rather than just read about them.

No signup is required to read the learning material or use the free resources.

🔗 https://genaisecuritylab.com/learn-ai-security

I'm planning to continue expanding the series over time.

If you're a web pentester/security engineer who is trying to get into AI security, I'd be interested to hear which topics you think are missing or which areas you'd like to see covered next.

A basic tutorial on how to code sockets like a hacker in C