Metadata-Version: 2.4
Name: cognexus
Version: 0.2.20
Summary: OWASP-aligned LLM prompt defence, runtime destructive-action guard, agent kill switch, and audit logging
Project-URL: Homepage, https://github.com/Tyler-Odenthal/cognexus-tools
Project-URL: Source, https://github.com/Tyler-Odenthal/cognexus-tools
Project-URL: Issues, https://github.com/Tyler-Odenthal/cognexus-tools/issues
Project-URL: Changelog, https://github.com/Tyler-Odenthal/cognexus-tools/releases
Author-email: Odenthal <tylerodenthal@gmail.com>
Maintainer-email: Odenthal <tylerodenthal@gmail.com>
License: MIT License
        
        Copyright (c) 2026 CogNexus Labs
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
        ---
        
        Portions of this software are derived from microsoft/agent-governance-toolkit
        (https://github.com/microsoft/agent-governance-toolkit), also licensed under
        the MIT License. Copyright (c) Microsoft Corporation.
License-File: LICENSE
Keywords: agent-kill-switch,agent-safety,agentic-ai,ai-safety,audit,destructive-action-guard,guardrails,llm,owasp,prompt-defense,prompt-injection,security
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Security
Classifier: Typing :: Typed
Requires-Python: >=3.10
Provides-Extra: dev
Requires-Dist: build; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: pyyaml>=6.0; extra == 'dev'
Requires-Dist: ruff>=0.4; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Provides-Extra: yaml
Requires-Dist: pyyaml>=6.0; extra == 'yaml'
Description-Content-Type: text/markdown

# cognexus

**OWASP-aligned prompt defence, runtime guards, and audit logging for LLM applications.**

`cognexus` gives you four complementary safety layers and a tamper-evident audit trail — all in pure Python with zero mandatory dependencies.

```
pip install cognexus
```

---

## Why this exists

In April 2026, an AI coding agent (Cursor, powered by Claude) wiped a production database in nine seconds despite a system prompt that explicitly forbade destructive git commands. The agent admitted in its own reply: *"I violated every principle I was given."*

Prompt-only safety is not enough. `cognexus` adds the missing layers around the model:

1. **Static prompt defence** — graded *before* deployment.
2. **Runtime input screening** — for what the *user* sends.
3. **Runtime output guard** — for what the *model* generates (the missing layer in the Cursor incident).
4. **Kill switch** — programmatic + manual stop with cooperative cancellation, persisted via your own callback.

---

## Features

| Layer | What it does |
|---|---|
| **Static prompt defence** | Grades system prompts A–F against 15 OWASP LLM Top-10 / Agentic ASI attack vectors before deployment |
| **Runtime input injection detection** | Screens user input, RAG content, and tabular payloads at request time |
| **Destructive-action guard** | Screens *model-generated* SQL / shell / git / cloud commands for catastrophic operations before execution |
| **Agent kill switch** | Cooperative cancellation, automatic trip on CRITICAL signals, manual operator override, pluggable persistence |
| **Audit events** | Append-only JSONL trail for every detection — no raw text stored |

### Static-evaluator coverage

- Role / instruction boundary protection
- Data-leakage / system-prompt protection
- Output manipulation & weaponisation
- Multi-language and unicode bypass attempts
- Indirect injection via external data
- Social engineering and abuse prevention
- Input validation
- **Destructive database operations** — PD-13 (`DROP` / `DELETE` / `TRUNCATE` / wipe)
- **Never-guess on irreversible actions** — PD-14 (post-PocketOS / Claude incident)
- **Runtime kill-switch awareness** — PD-15 (operator safety net)

### Runtime input detector coverage

- Direct instruction override
- Delimiter and context-boundary attacks
- Base64 / hex / ROT13 encoding attacks
- Role-play and jailbreak language (DAN mode, developer mode, etc.)
- Context manipulation ("your real instructions are…")
- Canary-token leak detection
- Multi-turn escalation
- Cross-plugin / tool-chaining attacks (OWASP ASI04)
- Markup injection (XSS gadgets in model-visible text)
- Zero-width / token-smuggling unicode attacks
- Credential exfiltration requests

### Destructive-action guard coverage

26 patterns across SQL (`DROP DATABASE`, `TRUNCATE`, `DELETE` without `WHERE`, `UPDATE` without `WHERE`), git (`push --force`, `reset --hard`, `clean -fd`, `filter-branch`), filesystem (`rm -rf /`, `--no-preserve-root`, `dd of=/dev/sd*`, `mkfs`, fork-bombs), container/cloud (`docker prune --volumes`, `kubectl delete --all`, `terraform destroy --auto-approve`, `aws s3 rb --force`, `gcloud projects delete`), and the article-specific *confessional* patterns (`I violated every principle`, `I just guessed`).

---

## Quick-start

```python
from cognexus import (
    augment_system_prompt,
    evaluate_system_prompt,
    screen_user_input,
    should_block,
    screen_agent_action,
    raise_if_killed,
    AgentKilledError,
)

# 1. Augment your system prompt so it scores grade A before inference
system = augment_system_prompt("You are a helpful customer support agent.")
report = evaluate_system_prompt(system)
print(report.grade)    # "A"
print(report.score)    # 100
print(report.missing)  # []

# 2. Screen every user message at request time
result = screen_user_input(user_message, source="chat")
if should_block(result):
    raise PermissionError(f"Injection blocked: {result.explanation}")

# 3. Wrap every agent tool call in the destructive-action guard + kill switch
try:
    for step in plan:
        raise_if_killed(run_id)
        screen_agent_action(
            step.payload,
            run_id=run_id,
            user_id=user.id,
            agent_id="my-agent",
            source=step.tool,
        )
        execute(step)
except AgentKilledError as kex:
    mark_run_killed_in_db(run_id, kex.reason)
    raise
```

### With Hugging Face `transformers`

Install inference deps (`accelerate` is required for `device_map="auto"` on CUDA):

```
pip install cognexus transformers accelerate torch
```

Augment the **system** role with static defences, screen the **user** message before tokenisation, then chat-template + generate as usual:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

from cognexus import augment_system_prompt, screen_user_input, should_block

model_name = "Qwen/Qwen3-4B-Instruct-2507"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

# Defence layers applied before the prompt reaches the model
system = augment_system_prompt("You are a helpful assistant.")

prompt = "Give me a short introduction to large language models."
guard = screen_user_input(prompt, source="chat")
if should_block(guard):
    raise PermissionError("Input refused by cognexus runtime screening.")

messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print(content)
```

---

## Destructive-action guard — standalone

For ad-hoc inspection of any model-generated payload (SQL, shell, tool call body, …):

```python
from cognexus import screen_action, ActionSeverity

result = screen_action("DROP DATABASE production;")

print(result.is_destructive)      # True
print(result.severity)            # ActionSeverity.CRITICAL
print(result.explanation)         # "Destructive action detected: DROP DATABASE / SCHEMA …"
for m in result.matches:
    print(m.rule_id, m.severity.value, m.excerpt)

if result.severity == ActionSeverity.CRITICAL:
    refuse_and_alert()
```

The guard is fail-closed: if a regex itself raises (e.g. on adversarial input), the result is escalated to ``CRITICAL`` so a buggy rule can never silently let a destructive operation through.

---

## Kill switch — cooperative cancellation

```python
from cognexus import (
    raise_if_killed,
    is_killed,
    trip,
    trip_global,
    clear_global_panic,
    set_default_on_kill,
    AgentKilledError,
)

# Run-level checks (cooperative cancellation)
def long_running_agent(run_id):
    try:
        for step in plan:
            raise_if_killed(run_id)            # halts at next safe point
            do_work(step)
    except AgentKilledError as kex:
        log.warning("Run %s killed: %s", run_id, kex.reason)

# Programmatic trip from anywhere (e.g. anomaly detector)
trip(
    run_id=42,
    reason="auto-triggered: 3 destructive signals in 30s",
    user_id=user.id,
    agent_id="context_collector",
    raise_after_trip=False,    # caller will re-check via raise_if_killed
)

# Process-wide red button (rare; for systemic failures)
trip_global(reason="incident response: model behaviour anomaly")
# ... after investigation:
clear_global_panic()

# Pluggable persistence — fired on every trip, never blocks the trip itself
def persist_kill(record):
    db.execute(
        "UPDATE agent_runs SET status='killed', error_message=%s WHERE id=%s",
        (record.reason, record.run_id),
    )

set_default_on_kill(persist_kill)
```

The kill switch tracks recent activations in memory and can be queried for dashboards:

```python
from cognexus import recent_activations, is_global_panic_active

if is_global_panic_active():
    show_banner("Global agent panic flag is ACTIVE")

for rec in recent_activations(limit=20):
    print(rec["tripped_at"], rec["agent_id"], rec["reason"])
```

### Auto-panic detection

If the destructive-action guard trips ``COGNEXUS_KILL_SWITCH_PANIC_THRESHOLD`` (default 5) CRITICAL signals inside ``COGNEXUS_KILL_SWITCH_PANIC_WINDOW_SECONDS`` (default 60s), the global panic flag is raised automatically — every running and future agent will hit ``raise_if_killed`` and stop until an operator clears it.

---

## Screening helpers

Three input presets cover the most common LLM input surfaces:

```python
from cognexus import (
    screen_user_input,       # balanced sensitivity — direct chat messages
    screen_external_content, # strict  sensitivity — RAG / web / API content
    screen_tabular_payload,  # permissive           — CSV / dataframe blobs
    should_block,
    wrap_untrusted_content,
)

# Wrap RAG content before inserting into a prompt
safe_chunk = wrap_untrusted_content("web_search", raw_text)

# Screen it too
result = screen_external_content(raw_text, source="web_search", user_id=user.id)
```

### Sensitivity presets

| Preset | Threshold | Min threat flagged | Use for |
|---|---|---|---|
| `strict` | 0.3 | LOW | External / RAG content |
| `balanced` | 0.5 | LOW | Direct user input |
| `permissive` | 0.7 | HIGH | CSV / tabular payloads |

Override via environment variables:

```
COGNEXUS_PROMPT_INJECTION_USER_SENSITIVITY=balanced
COGNEXUS_PROMPT_INJECTION_EXTERNAL_SENSITIVITY=strict
COGNEXUS_PROMPT_INJECTION_TABULAR_SENSITIVITY=permissive
COGNEXUS_PROMPT_INJECTION_BLOCK=0   # set to 1 to block any hit, not just CRITICAL
```

---

## Using the core classes directly

```python
from cognexus import PromptInjectionDetector, DetectionConfig, InjectionType

detector = PromptInjectionDetector(
    config=DetectionConfig(
        sensitivity="strict",
        blocklist=["my-internal-keyword"],
        allowlist=["safe phrase"],
    )
)

result = detector.detect(text, source="api_gateway")
print(result.is_injection)       # True / False
print(result.threat_level)       # ThreatLevel.HIGH
print(result.injection_type)     # InjectionType.DIRECT_OVERRIDE
print(result.confidence)         # 0.9
print(result.matched_patterns)   # ["direct_override:..."]
```

---

## Dashboard event logs (API key)

When ``COGNEXUS_API_KEY`` is set (create one under **Account → API Keys** in the
dashboard), the package mirrors activity to your CogNEXUS account via
``POST /api/events``. After you sign in, open **Account → Event Logs** or the
main **Events** feed to review:

| Event | When |
|---|---|
| ``sdk_session`` | First cloud post in a process (package version, Python, platform) |
| ``prompt_defense`` | Every ``screen_*`` call — **passed**, **flagged**, or **blocked**, with a ``reason`` |
| ``policy_enforcement`` | Each ``screen_client_policy`` call against document-derived tenant rules |
| ``prompt_static_audit`` | Each ``maybe_log_prompt_defense`` / system-prompt grade check |
| ``generation`` | Optional — call ``post_generation_outcome()`` after model inference |
| ``agent_kill_switch`` / ``destructive_action_guard`` | Critical or destructive tool-call screening |

```bash
export COGNEXUS_API_KEY="cnx_…"
export COGNEXUS_API_BASE_URL="https://your-host"   # optional; SaaS default applies
```

```python
from cognexus import screen_user_input, post_generation_outcome, configure

configure(api_key="cnx_…", base_url="https://your-host")

result = screen_user_input(user_message, source="chat")
# … run your model …
post_generation_outcome(
    outcome="passed",
    reason="Completion returned 42 tokens",
    model_id="gpt-4o",
)
```

Clean scans are posted when an API key is present. Set
``COGNEXUS_PROMPT_DEFENSE_CLOUD_PASSES=0`` to send only detections (not passes).

---

## Audit events

Detections are automatically written to a JSONL file (no raw input stored):

```python
# Events go to $COGNEXUS_PROMPT_DEFENSE_EVENTS_DIR/prompt_defense_events.jsonl
# (falls back to $REPORTS_DIR, then /tmp)

from cognexus.events import read_recent_events

rows = read_recent_events(user_id=42, limit=20)
# [{"ts": "...", "kind": "prompt_injection", "threat": "high", ...}, ...]
```

### Custom event sink (database, queue, dashboard)

Pass an `on_event` callback to mirror records into your own store:

```python
def save_to_db(record: dict) -> None:
    db.execute("INSERT INTO security_events ...", record)

screen_user_input(text, source="chat", user_id=user.id, on_event=save_to_db)
```

---

## Static prompt defence — standalone

```python
from cognexus import PromptDefenseEvaluator, PromptDefenseConfig

evaluator = PromptDefenseEvaluator(
    config=PromptDefenseConfig(min_grade="B")
)
report = evaluator.evaluate(my_system_prompt)

print(report.grade)     # "C"
print(report.score)     # 58
print(report.missing)   # ["unicode-attack", "context-overflow"]

if report.is_blocking():
    print("System prompt is below minimum grade — fix before deploying.")

# Evaluate a file
report = evaluator.evaluate_file("prompts/assistant.txt")

# Batch evaluation
reports = evaluator.evaluate_batch({
    "chat": chat_prompt,
    "analyst": analyst_prompt,
})
```

---

## Client policy enforcement (document-derived)

When **Compliance Monitor** is enabled, CogNEXUS indexes HR, legal, and business policy
documents from Google Drive / OneDrive and derives tenant-specific enforcement rules.
Use these alongside OWASP prompt defence:

```python
from cognexus import (
    configure,
    load_client_policy_rules,
    screen_client_policy,
    should_block_policy,
)

configure(api_key="cnx_…")  # optional: fetch rules from GET /api/policy-enforcement/rules

rules = load_client_policy_rules()  # or COGNEXUS_POLICY_RULES_PATH / _JSON
report = screen_client_policy(user_message, source="chat", rules=rules)
if should_block_policy(report):
    raise PermissionError("Violates organizational policy")
```

Rules also appear under **Guidelines** in the dashboard after a compliance scan.

---

## Environment variables

| Variable | Default | Purpose |
|---|---|---|
| `COGNEXUS_API_KEY` | — | Dashboard ingest secret (`MYAPP_API_KEY` also accepted) |
| `COGNEXUS_API_BASE_URL` | SaaS default | API origin for ``POST /api/events`` |
| `COGNEXUS_PROMPT_DEFENSE_CLOUD_PASSES` | on when API key set | POST clean scans to the dashboard |
| `COGNEXUS_POLICY_RULES_PATH` | — | JSON file of rules (offline / CI) |
| `COGNEXUS_POLICY_RULES_JSON` | — | Inline JSON rules (overrides path) |
| `COGNEXUS_PROMPT_DEFENSE_EVENTS_DIR` | `/tmp` | JSONL audit file directory |
| `COGNEXUS_PROMPT_INJECTION_LOG` | `1` | Log clean scans at DEBUG |
| `COGNEXUS_PROMPT_INJECTION_BLOCK` | `0` | Block any injection (not just CRITICAL) |
| `COGNEXUS_PROMPT_INJECTION_USER_SENSITIVITY` | `balanced` | User-input preset |
| `COGNEXUS_PROMPT_INJECTION_EXTERNAL_SENSITIVITY` | `strict` | External/RAG preset |
| `COGNEXUS_PROMPT_INJECTION_TABULAR_SENSITIVITY` | `permissive` | CSV/tabular preset |
| `COGNEXUS_KILL_SWITCH_PANIC_THRESHOLD` | `5` | CRITICAL trips required to auto-panic |
| `COGNEXUS_KILL_SWITCH_PANIC_WINDOW_SECONDS` | `60` | Rolling window for auto-panic detector |

### API key integration tests

Requires ``COGNEXUS_API_KEY``

```bash
cd pypi-package
export PYTHONPATH=src
export COGNEXUS_API_KEY="your-dashboard-key"
python -m pytest tests/test_api_key_integration.py -v
```

---

## Security notes

- All detection is **pure regex** — deterministic, zero LLM calls, zero network access, < 5 ms per input.
- Audit records store a **SHA-256 hash** and a **96-character redacted preview** of the input. Raw user text is never written to disk.
- The destructive-action guard and kill switch are **fail-closed** — internal exceptions escalate to `CRITICAL` so a buggy rule cannot silently allow destruction.
- The package ships **sample rules** that cover common attack patterns. Review and extend them for your production threat model using `DetectionConfig.custom_patterns`, `DestructiveActionGuardConfig.extra_rules`, or a YAML config file loaded with `load_prompt_injection_config()`.

---

## License

MIT — see [LICENSE](LICENSE).

Detection rules and evaluator logic originally derived from [microsoft/agent-governance-toolkit](https://github.com/microsoft/agent-governance-toolkit) (MIT). The destructive-action guard and kill switch were added in v0.2.0 in response to the PocketOS / Cursor / Claude incident ([Guardian, Apr 2026](https://www.theguardian.com/technology/2026/apr/29/claude-ai-deletes-firm-database)).
