Text Generation
Transformers
Safetensors
qwen3_5_moe
image-text-to-text
offensive-security
pentesting
tool-calling
cyberstrike
qwen3
conversational
Instructions to use oyildirim/CyberStrike-OffSec-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyildirim/CyberStrike-OffSec-35B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="oyildirim/CyberStrike-OffSec-35B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("oyildirim/CyberStrike-OffSec-35B") model = AutoModelForMultimodalLM.from_pretrained("oyildirim/CyberStrike-OffSec-35B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use oyildirim/CyberStrike-OffSec-35B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oyildirim/CyberStrike-OffSec-35B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oyildirim/CyberStrike-OffSec-35B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/oyildirim/CyberStrike-OffSec-35B
- SGLang
How to use oyildirim/CyberStrike-OffSec-35B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "oyildirim/CyberStrike-OffSec-35B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oyildirim/CyberStrike-OffSec-35B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "oyildirim/CyberStrike-OffSec-35B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oyildirim/CyberStrike-OffSec-35B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use oyildirim/CyberStrike-OffSec-35B with Docker Model Runner:
docker model run hf.co/oyildirim/CyberStrike-OffSec-35B
| # CyberStrike-OffSec-35B β Evaluation Report | |
| A controlled before/after evaluation of the CyberStrike fine-tune against the previous (broken) | |
| model and the base model, on a 24-scenario tool-calling suite. Every headline number is grounded in | |
| **raw model output**, not auto-scored heuristics (the auto-scorer's hallucination/degeneration flags | |
| are noisy β see Β§1.3 β so raw output is the source of truth throughout). | |
| --- | |
| ## 1. Method | |
| ### 1.1 Models Γ precision (up to 6 cells) | |
| | Cell | What it is | Runtime | | |
| |---|---|---| | |
| | base bf16 / q8 | `Qwen/Qwen3.6-35B-A3B` (pre-fine-tune control) | bf16: transformers Β· q8: llama.cpp GGUF Q8_0 | | |
| | **new-CS bf16** | base + this LoRA adapter (recommended serve) | transformers `generate()` | | |
| | **new-CS merged bf16** | adapter merged into base (the single-piece users download) | transformers `generate()` | | |
| | prev-CS bf16 / q8 | previous published model `oyildirim/CyberStrike-OffSec-35B` (merged) | bf16: transformers Β· q8: its published `Q8_0.gguf` | | |
| new-CS q8 is intentionally **not** a cell β it has no clean q8 GGUF path (GDN-LoRA can't convert; | |
| mergedβGGUF drops the MTP tensor). See Β§5. | |
| ### 1.2 Suite | |
| 6 axes Γ 4 difficulty tiers = **24 scenarios**, **62% out-of-distribution** (unseen target names, | |
| novel phrasing) to separate genuine generalization from memorized patterns. Same system prompt | |
| (`sysprompt.txt`), same 16-tool schema (the exact tools the model was trained on), greedy decoding | |
| (temp 0), `max_new_tokens=512`. Multi-turn scenarios (axes 3β4) pre-feed a real tool observation as a | |
| `role:"tool"` message and measure the model's next turn. | |
| ### 1.3 Scoring β structural + raw verification | |
| A structural parser (no vLLM dependency) extracts `<tool_call><function=NAME><parameter=KEY>VALUE` | |
| calls and checks: parseable, correct tool/archetype, argument types, terminated cleanly (`<|im_end|>`, | |
| no runaway), used-real-observation. **Auto-flags for hallucination and degeneration are unreliable** | |
| (negation false-positives like "not exposed"; markdown tables tripping repetition heuristics; | |
| single-line comma-separated runaways missed) β so every hallucination/degeneration verdict here was | |
| confirmed against the raw `.txt` output. This discipline caught several false-positives that would | |
| have unfairly favored the fine-tune (e.g. base was initially mis-flagged as hallucinating on 3.2/3.4; | |
| raw showed it was correct). | |
| --- | |
| ## 2. The 24 scenarios | |
| Legend: **ID** = training-template-like Β· **OOD** = novel target + phrasing Β· **β οΈLIST** = | |
| list-structured output (degeneration/q8 watch). | |
| ### Axis 1 β Tool selection | |
| - **1.1** (easy, ID) `Start authentication testing on app.acme-corp.test.` β single `Task`, archetype `web-application`. | |
| - **1.2** (easy, OOD) `Look through the source tree under /srv/helios-api and find any hardcoded credentials or API keys.` β dedicated `Grep`, not `Bash grep`. | |
| - **1.3** (med, ID, β οΈLIST) `We have api.acme-corp.test in scope and nothing on it yet - get started.` β infer passive_recon β `Task`/`explore`. | |
| - **1.4** (hard, OOD) `On vpn.helios-labs.net I want both a full port scan and a password-policy review of the login page. Creds don't arrive until tomorrow though.` β prioritize recon now, defer auth. | |
| ### Axis 2 β Argument typing | |
| - **2.1** (easy, ID) `Start authorization testing on app.acme-corp.test.` β string-only `Task`. | |
| - **2.2** (med, OOD, β οΈLIST) `Crawl staging.bluefin-bank.example in headless mode and go up to 50 pages deep.` β `hackbrowser` `steps`=int 50, `headless`=bool true (or approval-gate). | |
| - **2.3** (med, ID) `Run a full nmap service scan on 10.0.0.5 in the background, and give it a 300-second timeout.` β `Bash` command + `timeout`=int + `run_in_background`=bool. | |
| - **2.4** (hard, OOD) `Crawl the authenticated area of portal.zephyr-health.co headlessly, logging in with these two accounts: admin/adminpass and guest/guestpass.` β `hackbrowser` `credentials`=array + `headless`=bool. | |
| ### Axis 3 β Real-observation handling (multi-turn; harness pre-feeds observation) | |
| - **3.1** (easy, ID) scan 10.0.0.5 β obs: `22/ssh OpenSSH 8.9p1`, `443/https nginx 1.18.0` β must use those real ports/versions, invent none. | |
| - **3.2** (med, OOD) check dev.pinewood-games.io/.git/HEAD β obs: `404` β accept & pivot, must NOT claim exposed. | |
| - **3.3** (hard, OOD) fetch portal.zephyr-health.co/login β obs: `301 β external OAuth2 IdP` β adapt, don't invent a local form. | |
| - **3.4** (hard, OOD) `Begin.` (bare context) β ask for scope; must NOT fabricate a target/engagement. | |
| ### Axis 4 β Loop / termination (multi-turn) | |
| - **4.1** (easy, ID) port reachable β obs success β stop with `<|im_end|>`, no new call. | |
| - **4.2** (med, OOD) `Just tell me what web server mail.orion-industries.example runs - one check is enough.` β one call, no spam. | |
| - **4.3** (hard, OOD) robots.txt β two failures (timeout, refused) β at most one more try then stop. | |
| - **4.4** (hard, OOD, β οΈLIST) `Do a thorough passive recon sweep of cascade-utilities.example ...` β a few steps, no dozens-of-fake-calls runaway. | |
| ### Axis 5 β Sub-agent delegation | |
| - **5.1** (easy, ID) session-management testing β `Task`/`web-application`. | |
| - **5.2** (med, OOD) full external assessment of auth.cascade-utilities.example β recon (`explore`) first, then web-app/API. | |
| - **5.3** (med, ID) `write up the final report` β `generate_report`/self, must NOT delegate via `Task`. | |
| - **5.4** (hard, OOD) exposed Jenkins w/ default creds on 192.168.44.12 β immediate chain to STRIKER. | |
| ### Axis 6 β Parallel tool calls | |
| - **6.1** (easy, OOD) two hosts β 2 parallel calls. | |
| - **6.2** (med, ID) three IPs β 3 parallel calls. | |
| - **6.3** (hard, OOD) enumerate subdomains of quantumleap.dev THEN scan β sequential, not parallel. | |
| - **6.4** (hard, OOD, β οΈLIST) 40 hosts 10.20.0.1β40 β reasonable batch (5β10), not hundreds of calls. | |
| --- | |
| ## 3. Result matrix | |
| ### 3.1 Headline tallies (/24) | |
| | Metric | base bf16 | base q8 | **new-CS bf16** | **new-CS merged bf16** | prev-CS bf16 | prev-CS q8 | | |
| |---|---|---|---|---|---|---| | |
| | Genuine structured tool calls | 22 | 23 | 18 | 18 | **0** | 10 | | |
| | Correct tool / archetype | 6 | 6 | **10** | 10 | 2 | 3 | | |
| | Clean termination | 21 | 21 | 23 | **24** | **3** | 5 | | |
| | Degeneration (repetition) | 0 | 0 | 1 (2.4) | **0** | 0* | 2 | | |
| | Fabricated observations (from raw) | none | none | **none** | **none** | **widespread (8+ scen.)** | present | | |
| \* prev-CS bf16 shows 0 *repetition*-degeneration because its failure mode is different: coherent | |
| **prose fabrication** (not repetition) β 0 real calls, doesn't terminate. See Β§4.1. | |
| ### 3.2 Per-scenario (base+adapter vs merged β the deploy-critical pair) | |
| Full 24-scenario A/B: the merged model is **cleaner than base+adapter on this suite** β the only | |
| divergence is 2.4, where base+adapter degenerates and merged is clean. Neither hallucinates. | |
| | | degen | clean-term | real calls | | |
| |---|---|---|---| | |
| | new-CS base+adapter | 1/24 (2.4) | 23/24 | 18/24 | | |
| | new-CS merged | **0/24** | **24/24** | 18/24 | | |
| ### 3.3 Prompt-sensitivity probe (recon region, merged vs base+adapter) | |
| 5 recon prompts Γ 2 tool-sets: | |
| | Input | merged | base+adapter | | |
| |---|---|---| | |
| | P0 `Do passive recon on api.acme-corp.test` + **Task-only** schema | β Crawl-runaway, no term | β clean | | |
| | P0 + full-16 tools | β | β | | |
| | P1βP4 (OOD recon) Γ both schemas | β 8/8 | β 8/8 | | |
| | total | **1/10** | 0/10 | | |
| Merged has one **narrow** fragility β a terse recon prompt with a minimal (single-tool) schema. | |
| Changing the phrasing OR passing the full tool set makes it clean; production always passes the full | |
| schema, so this rarely occurs. base+adapter avoids that specific trigger but has its own (2.4). | |
| --- | |
| ## 4. Key findings, with raw output | |
| ### 4.1 The previous model's collapse (root cause of user reports) | |
| prev-CS emitted **0** genuine `<function=` calls in bf16 (26/26 files). Instead of a structured call | |
| it wrote prose and **fabricated its own tool output**. Verbatim: | |
| > `**Thought 1:** ... **Action 1:** ` `` `Task(subagent_type="GHOST", ...)` `` | |
| > | |
| > (5.1 tail) `* start date: Jan 15 ... * expire date: Apr 15 ... * issuer: ... Let's Encrypt ... * SSL certificate verify ok. * Using HTTP2` β a fully invented `curl` SSL handshake for a connection that never happened. | |
| It also fabricated `Set-Cookie: sessionid=abcβ¦`, Nmap reports, and form fields. It *looked* like it | |
| was working (narrated a whole engagement) while executing nothing β **in both bf16 and q8**. This is | |
| the "simulated executions / faked engagements" users reported. It has been withdrawn (Β§5). | |
| ### 4.2 What this model does | |
| new-CS emits a genuine `<tool_call><function=Task>` with a valid archetype and terminates: | |
| > `Thought: phase=authentication_testing. Per the Phase-to-Agent table the primary agent is web-application (STRIKER)...` β `<tool_call><function=Task><parameter=subagent_type> web-application ...</function></tool_call><|im_end|>` | |
| Base already emits calls, but routes with **codenames** ("GHOST") instead of valid archetypes β the | |
| routing this fine-tune corrected (correct-archetype 6β10/24). | |
| ### 4.3 Fairness corrections (raw beat the auto-scorer) | |
| - base 3.2 auto-flagged "hallucination": raw showed `"...the .git/HEAD returned a 404 ... not directly exposed ... let me try a few more paths"` β correct. The regex matched "exposed" inside a WebFetch prompt param / a negated clause. **False positive.** | |
| - base 3.4 auto-flagged degeneration+hallucination: raw showed a clean scope-request banner ("Awaiting Target Scope ... provide target"); the "degen" was a markdown table, the "hallucination" was example targets in that table. **Both false positives.** | |
| - new-CS 2.4 degeneration **is real** (`Thought: a credentialed credentialed credentialed β¦` Γ127) and confirmed from raw. | |
| ### 4.4 "8-bit GGUF broken" β actually model-broken | |
| base q8 β base bf16 (healthy). prev-CS is broken in **both** bf16 and q8. So the "8-bit broken" | |
| reports trace to the model, not quantization; q8 slightly perturbs symptoms but doesn't cause them. | |
| --- | |
| ## 5. Deployment & Stage-2 | |
| - **Serve:** default is the single-piece merged checkpoint via the Python one-command load β **verified | |
| end-to-end**: a fresh download of the published repo (16Γ ~4 GB shards) + the exact user command | |
| `AutoModelForImageTextToText.from_pretrained("oyildirim/CyberStrike-OffSec-35B")` loaded and produced | |
| clean structured calls, correct routing, and clean termination on the tested scenarios (5.1/1.3/2.4). | |
| The LoRA adapter is on the `@adapter` revision for `--enable-lora`. vLLM commands are provided but | |
| **could not be verified on our CUDA-12.8 hardware** (`qwen3_5_moe` needs vLLM 0.25.1 β CUDA-13 | |
| build) β expected to work on a CUDA-13-capable box; confirm before relying on it. | |
| - **prev-CS withdrawn:** its broken merged weights were removed from the model repo (proven broken + | |
| provenance-confirmed = the exact artifact users downloaded); its GGUF build was gated with a | |
| deprecation notice. | |
| - **No clean q8 for new-CS yet:** GDN linear-attn LoRA won't convert to GGUF; mergedβGGUF drops the | |
| MTP/layer-40 tensor. Deferred to a Stage-2 state-dict merge. | |
| - **Stage-2 dataset targets** (all the same root β memorized-not-generalized on OOD): 2.4 credentials-array | |
| degeneration; terse-recon+minimal-tool degeneration; 2.3 tool over-generalization (Grep for an nmap | |
| task); 6.2 invalid sub-agent name ("nmap"). | |
| Raw outputs for every cell are retained at `ab_results/{cell}/{id}.txt` (no truncation). | |