Evals for LLMs: Understanding Evaluation Systems for AI Models
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Main takeaways
Eval frameworks are like pen-tests for AI: They systematically benchmark LLM security, checking resilience to adversarial inputs, data poisoning, and jailbreaks before deployment.
Multiple dimensions matter: Modern evals combine metrics for hallucinations, bias, relevancy, and more, applied across the development lifecycle—from pre-production through CI/CD to production.
Red teaming complements structured evals: While evals offer automated, repeatable metrics, red teaming uncovers creative, real-world attack paths that structured tests may miss.
Embedding evals in CI/CD ensures safe agility: Integrating evals as automated guardrails helps catch regressions and enforce security and compliance continually as models evolve.
Snyk’s AI Trust Platform upholds security by design: It embeds AI-native security directly into development workflows to safely accelerate AI-driven innovation.
What are Evals?
Evals are systematic evaluation frameworks used to test how well large language models (LLMs) and AI systems perform against defined tasks, including security-relevant scenarios.
For cybersecurity experts, evals act like penetration tests for AI: they measure resilience to adversarial prompts, data poisoning, or jailbreak attempts, while also checking accuracy, reliability, and bias. By running structured evals, security teams can benchmark an AI model’s behavior, identify vulnerabilities in its responses, and ensure the system meets security, compliance, and risk management requirements before deployment.
Key components of modern evals
Multi-metric assessment: Beyond accuracy to include hallucination detection, bias measurement, and safety evaluations.
Iterative evaluation cycles: Start simple, then progressively add complexity.
Realistic datasets: Industry shift toward production-like test scenarios.
Failure mode analysis: Systematic identification of edge cases and vulnerabilities.
Evals vs red teaming
Evals | Red teaming | |
|---|---|---|
Purpose | Systematically test AI performance against predefined benchmarks and tasks. | Simulate real-world attacks to uncover unknown vulnerabilities. |
Approach | Structured, repeatable, often automated with scoring metrics. | Adversarial, creative, exploratory, often manual or semi-automated. |
Scope | Accuracy, reliability, bias, robustness against known attack types. | Exploitation potential, misuse scenarios, novel attack vectors. |
Output | Quantitative results (e.g., pass/fail rates, benchmarks, security scores). | Qualitative insights, attack paths, proof-of-concept exploits. |
Use Case | Model validation, compliance, ongoing monitoring. | Threat simulation, resilience testing, risk assessment. |
Strengths | Repeatable, measurable, scalable. | Finds unexpected vulnerabilities, simulates real adversary behavior. |
Limitations | May miss unknown attack vectors; limited creativity. | Time- and resource-intensive; less standardized. |
Integrating Evals in the CI/CD Pipeline
Integrating Evals into the CI/CD pipeline allows security teams to continuously validate the robustness of AI models throughout the development lifecycle.
Just as static analysis and dynamic testing gate traditional software builds, evals act as automated guardrails for AI, benchmarking models against adversarial prompts, jailbreak attempts, and domain-specific security tasks before deployment.
By embedding evals into CI/CD workflows, organizations can detect regressions in security posture early, enforce compliance requirements, and ensure that updated models maintain resilience against evolving threats, reducing the risk of pushing vulnerable AI components into production.
Types of evaluation approaches
When designing AI evaluation systems, we must carefully consider the balance between automated efficiency and human insight to ensure a comprehensive assessment of our models.
Automated vs. human-based methods
Automated evaluation offers scalability and consistency, enabling continuous monitoring across large datasets. However, human-in-the-loop (HITL) evaluation remains essential for quality control and nuanced assessments that require contextual understanding.
Benefits of automated methods:
Rapid processing of large-scale datasets
Consistent scoring criteria across evaluations
Real-time monitoring capabilities
Cost-effective for routine assessments
Limitations:
Limited contextual understanding
Difficulty capturing subjective quality metrics
Potential bias in automated scoring algorithms
Evaluation strategy categories
We can categorize our evaluation approaches into several strategic frameworks:
Black-box testing: Evaluating model outputs without examining internal processes, focusing on end-user experience.
White-box analysis: Deep inspection of model internals, attention mechanisms, and decision pathways.
Single-metric approaches: Concentrating on specific performance indicators like accuracy or BLEU scores.
Multi-dimensional evaluation: Comprehensive assessment across multiple criteria, including faithfulness, relevance, toxicity, and contextual precision.
For RAG systems specifically, multi-dimensional approaches are crucial to evaluate both retrieval quality and generation accuracy. Best practices include incorporating HITL stages in CI/CD pipelines for reviewing automated failures.
Frameworks like OpenAI Evals and Ragas offer specialized tooling for various evaluation needs, allowing us to select the most suitable methods based on our specific use cases and quality requirements.
Evaluation metrics and methodologies
Category | Key metrics and methodologies |
|---|---|
Reference-based vs. Reference-free | - Reference-based: Compare outputs to a ground truth using metrics like BLEU, ROUGE, METEOR, BERTScore, embedding similarity. - Reference-free: Use heuristic, statistical, or model-based scoring (e.g., regex patterns, text statistics, custom LLM judges). |
LLM-as-a-judge (Self-evaluation) | Use a strong LLM to score outputs against criteria such as factuality, coherence, and safety. Frameworks include G-Eval, DAG, QAG, GPTScore, and SelfCheckGPT. These combine statistical methods with intelligent scoring mechanisms. |
RAG-specific metrics | - Evaluation tailored to Retrieval-Augmented Generation systems: Faithfulness – factual consistency with retrieved context. - Answer Relevancy – relevance to prompt. - Contextual Relevancy / Recall – relevance and completeness of retrieved context. |
Classical statistical metrics | - BLEU, ROUGE, METEOR, LEPOR, and similar: measure n-gram overlap, fluency, and surface accuracy. - Perplexity: assesses prediction quality (lower indicates better). - Edit Distance (e.g. Levenshtein): measures minimal textual differences. |
Security-focused evaluation suites | - CyberSecEval 2: tests for prompt injection, code interpreter abuse, and introduces the safety-utility trade-off measured via False Refusal Rate (FRR); also assesses exploit generation capability. - CyberSecEval 3: adds evaluations for visual prompt injection, automated social engineering (spear phishing), and autonomous offensive cyber operations. - Broader frameworks highlight the need for routine, forward-looking security assessments. |
Holistic & ethical frameworks | S.C.O.R.E. (Safety, Consensus, Objectivity, Reproducibility, Explainability): a qualitative evaluation rubric especially useful in sensitive domains. |
Methodology across the evaluation lifecycle | - Pre-Production: Build evaluation datasets using synthetic examples, curated test cases, human annotation, and benchmarking. - CI/CD Pipelines: Employ automated tests, continuous monitoring, and guardrails to detect hallucinations or adversarial inputs. - Production: Use input/output validation, dynamic LLM guardrails, and RAG-specific evaluations for ongoing risk detection. |
Improve your AI security and evaluation systems with Snyk
Security remains critical in our evaluation strategy. But as this article highlights, running an eval is only a snapshot. The true power of evals is unlocked when you integrate and automate them as continuous guardrails within your CI/CD pipeline
This is where Snyk provides the critical link. While eval frameworks help you identify risks, Snyk's AI Trust Platform helps you enforce policy and prevent those risks from ever reaching production.
By embedding AI-native security guardrails directly into your development workflows, Snyk ensures the standards you test for with evals are automatically upheld. This approach moves you from simply evaluating risk to managing it—continuously, at scale, and without slowing down developers.
Don't just evaluate your AI, secure it. Learn more about the Snyk AI Trust Platform and turn your evaluation insights into automated protection.
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