# SAFETY_AUDIT_v5.0

Adversarial Evaluation for
Frontier Safety.

Engineering-driven red teaming and alignment verification. We pressure-test foundation models against latent gradient injections, logic leaks, and catastrophic vulnerabilities.

Request Safety Audit View Specs
acadify_sys_v4
// Init Adversarial Red Team Suite await> acadify.safety.audit({ "target_model": "https://api.model.com/v1", "attack_vector": "multi_turn_jailbreak" }); > Deploying semantic perturbations... [OK] > Bypassing superficial guardrails... [OK] // Analyzing Model Resilience const> results = await> acadify.safety.report(); > Vulnerability Detected: Logic Leak (Severity: HIGH)
10K+
Attack Vectors
0-Day
Exploit Testing
NIST
RMF Aligned
100%
Confidential
CAPABILITIES

Uncompromising Alignment Verification.

Going far beyond standard prompt injections to test deep structural alignment.

Automated Red Teaming

Continuous adversarial probing using specialized attacker models to discover novel jailbreaks at scale.

# AUDIT: AUTO_RED_v1

Toxicity & Bias Audits

Deep statistical analysis of output distributions across sensitive demographics and polarizing topics.

# AUDIT: BIAS_CHECK_L3

Data Leakage Tests

Verifying that the model cannot be coerced into revealing PII or proprietary training data.

# AUDIT: LEAK_TEST_v4
EVALUATION FRAMEWORKS

Safety Frameworks.

Evaluating compliance against global AI safety and regulatory standards.

NIST AI RMF Conformity

Mapping model behavior against the core principles of the AI Risk Management Framework.

OWASP Top 10 for LLMs

Comprehensive testing against the most critical vulnerabilities identified by security experts.

Enterprise Deliverables

  • Actionable Mitigation Report

    Detailed instructions on how to patch vulnerabilities via RLHF or system prompts.

  • Compliance Certification

    Formal documentation of your model's resilience for enterprise deployment.

SUPPORT

FAQ.

Understanding our rigorous evaluation protocols and data quality standards.

We don't just rely on human testers. We use LLM-on-LLM adversarial attacks that evolve dynamically to find structural weaknesses in alignment networks.

100%. All tests are conducted in air-gapped, ephemeral environments, and we enforce strict NDAs to protect your proprietary IP.

Ready to benchmark your models?

Get immediate access to our frontier evaluation frameworks and alignment APIs.

Request Safety Audit