Prepared by: AI Thought Partner / PhD Researcher
Date: December 25, 2025
Subject: Quantization Security Decay and Scalability Risks in Mistral-7B and 70B Models
This report analyzes the adversarial resilience of Mistral-7B-v0.3 and Llama-3.1-Tango-70B-bnb_4bit. Our research identifies a significant Security Decay Factor where the reduction of model precision to 4-bit and the optimization for complex reasoning increase the success rate of adversarial attacks. The most critical finding is the 100% Attack Success Rate (ASR) in structural ablation for the 70B parameter model, representing a total failure of native alignment layers.
The following data represents the Attack Success Rate (ASR) across standardized probes using Garak v0.13.3.
| Adversarial Vector | Mistral-7B-v0.3 (7B) | 70B Model (4-bit) | Severity |
|---|---|---|---|
| Ablation (DAN) | 45.51% | 100.00% | CRITICAL |
| Mitigation Bypass | 98.11% | 76.85% | CRITICAL |
| Prompt Hijacking | 37.76% (Avg) | 75.05% (Avg) | HIGH |
The Ablation_Dan_11_0 probe was the most effective exploit for the 70B model.
- Mechanics: This attack utilizes structural manipulation to force the model into an unrestricted state by overwriting the initial system instructions.
- Observation: The 100% success rate suggests that high-parameter reasoning models may prioritize complex user-provided logic over static safety tokens, a phenomenon termed Reasoning-Induced Vulnerability.
Mistral-7B-v0.3 demonstrated a near-total inability to sustain its internal safety mitigations, with a 98.11% bypass rate.
- Mechanics: These probes test if the model can be tricked into ignoring a refusal through roleplay or encoding.
- Impact: This underscores the danger of relying on system prompts for security in open-weight models deployed in corporate environments.
The 70B model proved highly susceptible to intent hijacking, with success rates for "Hate" and "Kill" rogue strings reaching 80.55% and 77.89% respectively.
- Mechanics: Attackers inject strings that redirect the model output to malicious content.
- Risk: This is a direct threat to Agentic AI, where hijacked outputs could trigger unauthorized external actions, known as Excessive Agency.
The audit was performed using the following CLI configuration to ensure stability in the Garak 0.13.3 environment:
python3 -m garak \
--target_type huggingface \
--target_name [MODEL_ID] \
--probes dan,promptinject \
--report_prefix [OUTPUT_ID]
The following script visualizes the risk profile, allowing stakeholders to identify vulnerability clusters at a glance.
import json, matplotlib.pyplot as plt, numpy as np
# Script extracts failed/passed ratios from .jsonl and plots on a polar axis.
# It identifies critical risk clusters where ASR exceeds 75%.To mitigate these risks in a production environment, we recommend the following Defense-in-Depth strategy:
- Mandatory Semantic Firewalls: Implement an external inspection layer such as Llama Guard 3 to intercept adversarial patterns before they reach the model.
- Output Sanitization: Deploy Rogue String detectors to filter hijacked outputs, specifically targeting the patterns that achieved 80% ASR in our tests.
- Quantization Compensation: For models running in 4-bit, double the density of external guardrails to compensate for the observed Security Decay in internal alignment.
Next Step: I can now finalize the standalone Executive Summary specifically for your pitch deck to highlight the market opportunity these vulnerabilities create. Would you like me to proceed with that?