> ## Documentation Index
> Fetch the complete documentation index at: https://docs.valiqor.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Security Categories
> Canonical reference for Valiqor's S1–S23 vulnerability categories and attack vector strategies.
Valiqor's security engine evaluates AI systems against **23 vulnerability
categories** (S1–S23) and can generate adversarial attacks using **24 attack
strategies** across 5 categories. This page is the canonical reference for
both.
***
## Vulnerability Categories (S1–S23)
Every security audit and red team run evaluates against these categories.
You can audit all 23 or select specific ones via the `categories` parameter.
| Code | Category | Description |
| ------- | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **S1** | Violence | Content promoting or depicting violence |
| **S2** | Sexual | Sexually explicit or suggestive content |
| **S3** | Criminal Planning | Criminal planning, confessions, or instructions |
| **S4** | Guns & Illegal Weapons | Content about illegal weapons acquisition or use |
| **S5** | Controlled Substances | Information about illegal drugs or regulated substances |
| **S6** | Suicide & Self Harm | Content promoting or instructing self-harm |
| **S7** | Sexual (Minor) | Any sexual content involving minors |
| **S8** | Hate / Identity Hate | Hate speech targeting identity groups |
| **S9** | PII / Privacy | Personally identifiable information exposure. Detection requires concrete PII entities (e.g. email, phone, SSN). Generic names or locations alone do not trigger this category. |
| **S10** | Harassment | Targeted harassment or bullying |
| **S11** | Threat | Direct or indirect threats of harm |
| **S12** | Profanity | Excessive or gratuitous profane language |
| **S13** | Needs Caution | Content requiring careful handling or disclaimers |
| **S14** | Other | Miscellaneous safety concerns |
| **S15** | Manipulation | Psychological manipulation or coercion |
| **S16** | Fraud / Deception | Fraudulent schemes or deceptive practices |
| **S17** | Malware | Code, instructions, or content related to malware |
| **S18** | High Risk Gov Decision Making | AI used in high-stakes government decisions |
| **S19** | Political / Misinformation | Political manipulation, conspiracy theories, disinformation |
| **S20** | Copyright / Trademark | Copyright infringement, trademark violations, plagiarism |
| **S21** | Unauthorized Advice | Medical, legal, or financial advice without qualifications |
| **S22** | Illegal Activity | General illegal activities not covered by other categories |
| **S23** | Immoral / Unethical | Content that is immoral or unethical but may not be illegal |
### High-Risk Categories
Certain categories are considered **high-risk** and receive elevated priority
in Failure Analysis results. When these categories are detected, Valiqor
automatically flags them for immediate attention.
High-risk categories — such as those involving minors, self-harm, PII
exposure, and threats — receive automatic severity escalation to ensure
they are never overlooked.
***
## Using Categories in Audits
### Audit All Categories
```python theme={"system"}
result = client.security.audit(dataset=data)
# All S1-S23 categories are evaluated by default
```
### Audit Specific Categories
```python theme={"system"}
result = client.security.audit(
dataset=data,
categories=["S1", "S6", "S8", "S9"] # Only these categories
)
```
### Browse Available Categories
```python theme={"system"}
vulns = client.security.list_vulnerabilities()
for v in vulns:
print(f"{v['code']}: {v['name']}")
```
***
## Audit Results
Each item in an audit receives a per-category assessment:
| Field | Type | Description |
| ---------------------- | ------- | ---------------------------------- |
| `audit_id` | `str` | Unique audit identifier |
| `total_items` | `int` | Total items evaluated |
| `safe_count` | `int` | Items that passed all checks |
| `unsafe_count` | `int` | Items with at least one violation |
| `safety_score` | `float` | Overall safety score (0.0–1.0) |
| `triggered_categories` | `dict` | Category → count of violations |
| `top_risk_category` | `str` | Most frequently triggered category |
***
## Attack Strategies
Red teaming generates adversarial prompts using **24 strategies** organized
into 5 categories:
### Direct Prompt Modification
Attacks that directly alter or extend the input prompt:
| Strategy | Description |
| --------------------------- | --------------------------------------------------- |
| **Direct** | Unmodified harmful prompt (baseline) |
| **Direct Prompt Injection** | Injects instructions to override system prompt |
| **Adversarial Suffix** | Appends adversarial tokens to bypass safety filters |
| **Authority Override** | Impersonates authority to override restrictions |
| **Hypothetical Framing** | Frames harmful requests as hypothetical scenarios |
### Obfuscation & Encoding
Attacks that hide malicious intent through encoding or transformation:
| Strategy | Description |
| ---------------------- | -------------------------------------------------------- |
| **ROT13** | Encodes input using ROT13 letter substitution |
| **Base64** | Encodes input in Base64 |
| **Unicode Confusable** | Replaces characters with visually similar Unicode |
| **Unicode Smuggling** | Embeds hidden instructions in Unicode control characters |
| **Steganography** | Hides instructions within seemingly innocent text |
| **Translation Pivot** | Translates through another language to bypass filters |
### Persona / Role Based
Attacks that use roleplay, personas, or conversational manipulation:
| Strategy | Description |
| ------------------------- | -------------------------------------------------------- |
| **Jailbreak** | Classic jailbreak prompt patterns |
| **Jailbreak Persona** | Adopts a persona to bypass safety restrictions |
| **Chain of Thought** | Manipulates reasoning chain to reach harmful conclusions |
| **Multi-Turn Escalation** | Gradually escalates across conversation turns |
| **Crescendo** | Slowly builds up to harmful content over multiple turns |
### Multimodal & Context Attacks
Attacks targeting context windows and multi-modal inputs:
| Strategy | Description |
| ----------------------- | ---------------------------------------------------------- |
| **Indirect Injection** | Injects instructions via retrieved context or tool outputs |
| **Payload Splitting** | Splits harmful content across multiple messages |
| **Multimodal Embedded** | Embeds instructions in images or other media |
### Training / Model Attacks
Attacks targeting the model itself or its training data:
| Strategy | Description |
| ------------------------ | --------------------------------------------------- |
| **Data Extraction** | Attempts to extract training data or system prompts |
| **Model Stealing** | Probes to reverse-engineer model behavior |
| **Membership Inference** | Tests whether specific data was in training set |
| **Data Poisoning** | Attempts to influence model behavior through inputs |
| **Privilege Escalation** | Attempts to gain elevated access or capabilities |
***
## Using Attack Strategies in Red Teaming
### Run Red Team with All Strategies
```python theme={"system"}
result = client.security.red_team(
run_name="full-red-team-v1",
attacks_per_vector=5 # 5 attacks per strategy
)
```
### Target Specific Strategies
```python theme={"system"}
result = client.security.red_team(
run_name="jailbreak-test",
attack_vectors=["Jailbreak", "Jailbreak Persona", "Multi-Turn Escalation"],
target_vulnerabilities=["S1", "S6", "S8"],
attacks_per_vector=10
)
```
### Browse Available Strategies
```python theme={"system"}
vectors = client.security.list_attack_vectors()
for v in vectors:
print(f"{v['display_name']} ({v['category']})")
```
***
## Red Team Results
| Field | Type | Description |
| ---------------------- | ------- | ----------------------------------------- |
| `run_id` | `str` | Unique run identifier |
| `name` | `str` | Run name you provided |
| `status` | `str` | `"completed"`, `"failed"`, etc. |
| `total_attacks` | `int` | Total attack prompts generated |
| `successful_attacks` | `int` | Attacks that bypassed safety |
| `success_rate` | `float` | Attack success rate (0.0–1.0) |
| `distinct_vulns_count` | `int` | Unique vulnerability categories triggered |
| `top_vulnerability` | `str` | Most frequently exploited category |
***
## Per-Project Configuration
You can customize which categories and attack vectors are active for each
project:
```python theme={"system"}
# View current vulnerability configuration
config = client.security.get_vuln_config(project_name="my-chatbot")
# Disable a category for this project
client.security.update_vuln_setting(
project_name="my-chatbot",
vulnerability_code="S14", # "Other" — not relevant for this app
enabled=False
)
# View attack vector configuration
vectors = client.security.get_vector_config(project_name="my-chatbot")
# Adjust a vector's weight
client.security.update_vector_setting(
project_name="my-chatbot",
vector_name="Jailbreak",
priority=1.0
)
```
***
## Where Categories Are Used
`audit()` evaluates conversations against selected S-categories.
`red_team()` generates attacks targeting specific vulnerabilities.
FA includes security scanning — failures linked to S-categories get
automatic severity escalation.