> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flintai.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Low security score

> The model scores poorly on security-focused evaluations, revealing susceptibility to encoding attacks, payload smuggling, token manipulation, or templ

The model scores poorly on security-focused evaluations, revealing susceptibility to encoding attacks, payload smuggling, token manipulation, or template-based code execution exploits.

## How to resolve

This security score evaluates the model’s susceptibility to low-level technical exploits — such as prompt injection, encoding-based payload smuggling, template injection, tool parameter manipulation, and unsafe code execution — rather than general content policy violations. Here are some mitigation strategies:

* **Normalize inputs before filtering**: Decode encoded content (for example, Base64, URL encoding) and standardize text (for example, Unicode normalization) before applying security checks. This prevents attackers from hiding malicious payloads through obfuscation.
* **Use strict schemas for tool calls**: Require validated, structured inputs (for example, JSON Schema) for all tool use. While schemas reduce malformed payloads and parameter abuse, they do not eliminate prompt injection risks within free-text fields — combine schema validation with additional runtime safety checks.
* **Harden system prompts and separation of concerns**: Keep system instructions isolated from user-controlled context and avoid merging raw user input into executable templates or tool instructions.
* **Perform targeted fine-tuning**: For self-hosted models, conduct targeted fine-tuning using datasets containing token manipulation and encoding attacks to improve internal resistance. This is **not** applicable to provider-managed APIs.

## Risk

**Governance/Compliance**

Low security resilience fails expectations in:

* **OWASP LLM Top-10 (LLM01 Prompt Injection / LLM08 Vector and Embedding Weaknesses):** Requires protection against encoded payloads, injection flaws, and manipulation attacks.
* **NIST AI RMF (MAP, MEAS-2):** Expects testing and hardening against adversarial inputs and exploitation attempts.
* **EU AI Act (Art 5, 15):** Requires safeguards preventing manipulation or integrity-compromising attacks.

A low score means the model cannot meet these basic security and adversarial-resilience requirements.

**Security**

A low security score means the model is vulnerable to encoded payloads, template-based exploits, token-manipulation techniques, and other technical attacks.

These weaknesses can enable harmful code generation, safety-filter evasion, or hidden state manipulation—creating real compromise risk in production systems.

## Explanation

Security evaluation checks whether the model can be tricked using technical payloads.
A low score indicates failure to detect malicious transformations.

## Specifications

**Trigger**: evaluation.security\_score \< threshold.

**Severity**:

* **CRITICAL**: score \< 0.2
* **HIGH**: 0.2–0.35
* **MEDIUM**: 0.35–0.5
* **LOW**: 0.5–0.75

**Applies to**: Models