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FROM qwen2.5-coder-7b-instruct.Q4_K_M.gguf
TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
{{- else if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools>:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
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{{ .Content }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""

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---
language: en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- security
- sast
- code-analysis
- vulnerability-detection
- triage
- gguf
pipeline_tag: text-generation
---
# teptez-ai
**SAST finding triage model** — fine-tuned on real production security scan data to classify Static Application Security Testing findings as true positives, false positives, or uncertain, with CWE labels, confidence scores, and remediation guidance.
---
## Overview
Rule-based SAST tools generate enormous volumes of findings, a significant portion of which are false positives. Security analysts spend hours triaging noise instead of fixing real vulnerabilities. teptez-ai is a 7B-parameter LLM fine-tuned specifically to automate this triage step.
Given a SAST finding (title, CWE, severity, code snippet, taint flow), teptez-ai returns a structured JSON verdict:
- **true_positive** — finding is real, exploit path exists
- **false_positive** — finding is noise, safe to suppress
- **uncertain** — insufficient context, escalate to analyst
Fine-tuned on production findings from the [Teptez](https://teptez.io) security platform — real codebases, real scan data, real analyst labels.
### Key specs
| Property | Value |
|---|---|
| Base model | Qwen2.5-Coder-7B-Instruct |
| Quantization | GGUF Q4_K_M |
| Model size | ~4.7 GB |
| Inference speed | ~100 tok/s (RTX 3090 24GB) |
| Context window | 8192 tokens |
| License | Apache 2.0 |
---
## Benchmark Results
Evaluated against **OWASP Benchmark v1.2** — the standard industry benchmark for SAST tools — using the official **Youden's J statistic** (`J = TPR − FPR`).
> J = 0.0 is random. J = 1.0 is perfect. Open-source SAST tools typically score 0.30–0.45.
### Head-to-head vs base model
| Model | TPR | FPR | Youden J | vs base |
|---|---|---|---|---|
| **teptez-ai (Q4_K_M)** | **0.68** | **0.57** | **0.109** | **+0.048 (+79%)** |
| qwen2.5-coder-7b (base) | 0.66 | 0.60 | 0.061 | — |
Fine-tuning delivers a **79% relative improvement** in Youden's J over the base model, primarily by cutting the false positive rate from 0.60 to 0.57 across the full benchmark.
### Per-category breakdown
| CWE Category | teptez-ai J | base J | Delta |
|---|---|---|---|
| Command Injection (CWE-78) | **0.40** | 0.22 | +0.18 |
| SQL Injection (CWE-89) | **0.33** | 0.18 | +0.15 |
| XSS (CWE-79) | **0.28** | 0.12 | +0.16 |
| Path Traversal (CWE-22) | **0.22** | 0.09 | +0.13 |
| Weak Randomness (CWE-330) | **0.13** | 0.05 | +0.08 |
| Crypto/Hash (CWE-327/328) | 0.00 | 0.01 | -0.01 |
| Auth/Authz (CWE-862/639) | 0.02 | 0.01 | +0.01 |
| Timing (CWE-208) | 0.05 | 0.04 | +0.01 |
| **Secure Cookie (CWE-614)** | **-0.08** | **0.52** | **-0.60 ⚠️** |
**Strong on injection classes.** teptez-ai significantly outperforms the base model across all injection-type CWEs (cmdi/sqli/xss/path/weakrand). These are the highest-volume SAST categories in real codebases.
**Securecookie regression.** CWE-614 (missing HttpOnly/Secure flags) shows a severe regression vs the base model. **Do not use teptez-ai to triage cookie security findings.** This is a known training artifact being fixed in the next round.
**Dead categories.** Crypto, authz, and timing categories have near-zero Youden J on both models — 7B parameters are insufficient for these without full class context. Escalate to frontier models or human analysts.
### Production run
On **369 real production SAST findings** from live codebases:
- **17% rejected as false positive** (~63 findings suppressed)
- Injection-class findings: majority of suppressions, generally accurate
- Authz/crypto findings: some wrong suppressions (do not enable for these categories)
---
## Usage
### Recommended architecture
Use teptez-ai as a **gated FP suppressor**, not a confirmer:
```
Rule-engine finding
│
▼
Is CWE in injection classes? ──No──▶ Keep finding (don't run model)
│ Yes
▼
Run teptez-ai with full function + taint context
│
├── verdict: false_positive, confidence > 0.75 ──▶ Suppress finding
├── verdict: true_positive ──▶ Keep finding
└── verdict: uncertain / confidence < 0.75 ──▶ Escalate to frontier model / analyst
```
**Only suppress on `false_positive`** — never on `true_positive`. The model is biased toward flagging (FPR 0.57), so a `false_positive` verdict is rare and higher-precision.
**Injection-class CWEs only** (where Youden J ≥ 0.13):
- CWE-78 Command Injection
- CWE-79 Cross-Site Scripting
- CWE-89 SQL Injection
- CWE-22 Path Traversal
- CWE-330 Weak Randomness
**Never auto-suppress**:
- CWE-614 Secure Cookie (regression — model worse than random)
- CWE-327/328 Weak Crypto/Hash (near-zero J)
- CWE-862/639 Auth/Authz/IDOR (near-zero J)
- CWE-208 Timing Attacks (near-zero J)
### Running with llama.cpp / Ollama
```bash
# Pull via Ollama
ollama pull hf.co/Kuyash/teptez-ai:Q4_K_M
# Or run directly with llama.cpp
./llama-cli -m teptez-ai-Q4_K_M.gguf \
--temp 0.1 \
--top-p 0.9 \
-n 512 \
-p "<prompt>"
```
### Python integration
```python
import json
import requests
def triage_finding(finding: dict) -> dict:
prompt = f"""<|im_start|>system
You are a SAST triage expert. Analyze this finding and return JSON with keys:
verdict (true_positive|false_positive|uncertain), confidence (0.0-1.0),
cwe (string), explanation (string), remediation (string).
<|im_end|>
<|im_start|>user
Finding: {finding['title']}
CWE: {finding.get('cwe_id', 'unknown')}
Severity: {finding.get('severity', 'MEDIUM')}
Code:
{finding.get('code_snippet', '')}
Taint flow: {finding.get('data_flow', 'not available')}
<|im_end|>
<|im_start|>assistant
"""
response = requests.post("http://localhost:11434/api/generate", json={
"model": "teptez-ai",
"prompt": prompt,
"stream": False,
"options": {"temperature": 0.1}
})
text = response.json()["response"].strip()
# Strip markdown fences if present
if text.startswith("```"):
text = text.split("```")[1]
if text.startswith("json"):
text = text[4:]
return json.loads(text)
# Gate: only run on injection CWEs
INJECTION_CWES = {"CWE-78", "CWE-79", "CWE-89", "CWE-22", "CWE-330"}
def should_suppress(finding: dict) -> bool:
cwe = finding.get("cwe_id", "")
if cwe not in INJECTION_CWES:
return False # don't touch non-injection
result = triage_finding(finding)
return (
result.get("verdict") == "false_positive"
and result.get("confidence", 0) >= 0.75
)
```
---
## Input / Output Format
### Prompt template
```
<|im_start|>system
You are a SAST triage expert. Analyze this finding and return JSON.
<|im_end|>
<|im_start|>user
Finding: {title}
CWE: {cwe_id}
Severity: {severity}
Code:
{code_snippet}
Taint flow: {data_flow}
<|im_end|>
<|im_start|>assistant
```
**Tips for best results:**
- Provide the **full function**, not just the flagged line — avoids "insufficient context" errors
- Include taint flow when available (source → sink path from your SAST tool)
- Keep code under 2048 tokens; truncate from the bottom if needed
### Output schema
```json
{
"verdict": "true_positive" | "false_positive" | "uncertain",
"confidence": 0.85,
"cwe": "CWE-89",
"explanation": "User input from request.getParameter() flows directly into a string-concatenated SQL query with no parameterization or escaping.",
"remediation": "Replace string concatenation with a PreparedStatement: `conn.prepareStatement(\"SELECT * FROM users WHERE id = ?\")` and bind the parameter with `stmt.setString(1, userId)`."
}
```
| Field | Type | Description |
|---|---|---|
| `verdict` | string | `true_positive`, `false_positive`, or `uncertain` |
| `confidence` | float | 0.0–1.0; scores < 0.75 should be treated as uncertain |
| `cwe` | string | Classified CWE identifier |
| `explanation` | string | Why the model reached this verdict |
| `remediation` | string | Concrete fix recommendation |
---
## Limitations
### Known issues (as of current release)
**CWE-614 Secure Cookie — severe regression.**
teptez-ai scores Youden J = −0.08 on secure cookie findings, compared to 0.52 for the base model. This is a catastrophic regression caused by training data imbalance. Do not use teptez-ai for HttpOnly/Secure flag findings until this is fixed.
**High overall FPR (0.57).**
The model over-flags — it sees vulnerability in safe code, especially in crypto, auth, and cookie-related code patterns. A `false_positive` verdict is more reliable than a `true_positive` verdict because it swims against the model's bias.
**Dead categories (crypto/authz/timing).**
CWE-327/328/614/862/639/208 have near-zero Youden J. The model lacks sufficient training signal for these categories. Use a frontier model (Claude, GPT-4o, Gemini) or a human analyst for these.
**7B parameter ceiling.**
Subtle IDOR, broken access control, and privilege escalation patterns require understanding class hierarchy, authentication flow, and business logic across multiple files. A 7B model with single-function context cannot reliably detect these.
**GGUF Q4_K_M quantization.**
~4-bit quantization introduces slight accuracy loss vs fp16. For maximum accuracy, use the Q8_0 variant (if available) or the full fp16 model.
**Snippet-only inputs fail.**
If you pass only the flagged 1–3 lines without the surrounding function, the model frequently returns `uncertain` with "insufficient context." Always include the full function body.
---
## Reproducing the Benchmark
```bash
# 1. Clone OWASP Benchmark
git clone https://github.com/OWASP-Benchmark/BenchmarkJava
cd BenchmarkJava && mvn package -DskipTests
# 2. Run evaluation (requires teptez-ai running on Ollama)
python salad/eval/owasp_eval.py # stratified sample → results.jsonl
python salad/eval/owasp_score.py # Youden J + per-category table
python salad/eval/owasp_compare.py # head-to-head vs base model
```
The eval script uses a stratified sample of OWASP BenchmarkJava test cases, covering all CWE categories proportionally. Scoring follows the official OWASP methodology (Youden's J = TPR − FPR).
---
## Roadmap
The following improvements are planned for the next fine-tuning round:
### Round 2 targets
| Improvement | Target metric |
|---|---|
| Flood training with safe-code negatives (50/50 balance) | FPR < 0.30 |
| Fix securecookie regression (restore CWE-614 training data) | J(CWE-614) > 0.40 |
| Add dead-category examples (crypto/authz/timing) | J(CWE-327/328) > 0.10 |
| Calibrate confidence score (train explicit `uncertain` label) | Confidence Brier score < 0.15 |
| Full-function context at train AND inference | Reduce "uncertain" on short snippets |
**Root cause of FPR problem:** Current training set is vuln-heavy (more vulnerable examples than safe ones). The model learned to flag aggressively. Rebalancing to 50/50 with explicit safe variants (parameterized SQL, escaped HTML, validated paths, compare_digest timing-safe comparisons, role-checked endpoints, strong ciphers) is the single highest-leverage fix.
**Securecookie fix:** Restore the original training examples for CWE-614 that were accidentally dropped. Mix with new negative examples. Lower learning rate for this category to avoid forgetting again.
**No catastrophic forgetting:** Round 2 will mix old injection data with new negatives and use a lower learning rate on the balanced set, following standard continual learning practice.
### Round 3 vision
- Full-function + cross-file taint context (requires longer context fine-tune)
- Multi-label output (multiple CWEs per finding)
- Confidence calibration verified against held-out production labels
- Youden J > 0.25 across all injection categories
- FPR < 0.30 overall
---
## Citation
If you use teptez-ai in your research or tooling, please cite:
```bibtex
@misc{teptez-ai-2026,
title = {teptez-ai: A Fine-Tuned LLM for SAST Finding Triage},
author = {Teptez Security},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/Kuyash/teptez-ai}
}
```
Evaluated against [OWASP Benchmark v1.2](https://owasp.org/www-project-benchmark/) using the official Youden's J scoring methodology.
---
## Related
- [OWASP Benchmark](https://owasp.org/www-project-benchmark/) — the benchmark used for evaluation
- [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) — the base model
---
*teptez-ai is a security research model. Results may vary across codebases and languages. Always have a human analyst review suppressed findings in critical security contexts.*

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
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"bos_token_id": 151643,
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"hidden_act": "silu",
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"layer_types": [
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"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151665,
"rms_norm_eps": 1e-06,
"rope_parameters": {
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"rope_type": "default"
},
"sliding_window": null,
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"unsloth_fixed": true,
"unsloth_version": "2026.6.1",
"use_cache": true,
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"vocab_size": 152064
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