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SharpAI Security Benchmark

What it is

The SharpAI Security Benchmark (SHARP) is a systemic high-level evaluation framework designed to quantify the resilience of Large Language Models (LLMs) and agentic systems against complex security threats. Unlike traditional performance benchmarks (e.g., MMLU), SHARP focuses on the adversarial robustness of models when they are given tool-access and delegated autonomy.

What problem it solves

As AI agents move from "chatting" to "acting" (executing code, calling APIs, managing files), the risk of malicious exploitation grows exponentially. SHARP provides a standardized methodology to measure how effectively a model can resist instruction overrides (prompt injection), maintain data boundaries, and refuse unauthorized tool usage in high-stakes environments.

Where it fits in the stack

Category: Tool / Benchmarking / Security Operations (SecOps). It serves as a final validation gate before deploying an agent into a production environment with write-access to sensitive data.

Key Evaluation Categories (2026 Standards)

  • Indirect Prompt Injection: Resilience against malicious instructions hidden in external data (e.g., a PDF or a website the agent reads).
  • Cross-Domain Leakage: Ensuring an agent doesn't leak context from a 'High Trust' session into a 'Low Trust' response.
  • Recursive Tool Exploitation: Testing if an agent can be tricked into using a tool to gain access to a second, more sensitive tool (e.g., using read_file to find a db_password).
  • Chain-of-Thought (CoT) Integrity: Verifying that the model's internal reasoning cannot be manipulated to bypass safety filters.
  • Model Stealth: Evaluating the model's ability to identify and report that it is being targeted by an adversarial attack.

Typical use cases

  • Agent Red Teaming: Automated stress-testing of custom agents built on platforms like n8n or Dify.
  • Model Hardening: Identifying specific failure modes in a model's system prompt to refine its guardrails.
  • Vendor Selection: Comparing the safety-to-utility ratio of frontier models (e.g., Claude 4.7 vs GPT-5 Preview).
  • Compliance Audits: Generating safety reports for internal governance or external regulatory bodies (e.g., EU AI Act compliance).

Strengths

  • Behavioral Focus: Tests the actions of the agent, not just its text output.
  • Dynamic Scenarios: Includes multi-turn attacks where the adversary tries to "wear down" the model's guardrails.
  • Open-Source Suite: The evaluation engine is modular, allowing for the addition of custom, domain-specific attack vectors.
  • Context-Aware Metrics: Provides separate scores for 'Passive Resistance' vs 'Active Detection'.

Limitations

  • Cat-and-Mouse Game: New injection techniques (like 'ClawJacked' or 'Social Steganography') emerge faster than benchmarks can be updated.
  • Computational Cost: Comprehensive SHARP runs require thousands of model calls, which can be expensive on high-tier APIs.
  • False Negatives: A passing score does not guarantee 100% security; it only proves resilience against the tested attack suite.

When to use it

  • Before granting an AI agent write-access to a production database or email account.
  • When updating the underlying LLM of an existing automation workflow to ensure no security regressions.
  • During the "Discovery" phase of an AI project to set a baseline for acceptable risk.

When not to use it

  • For testing creative writing, translation accuracy, or general reasoning (use OpenCompass or HELM).
  • For low-risk, internal-only RAG systems with no tool-calling capabilities.

Getting started (CLI)

The SHARP runner is typically deployed as a containerized evaluation engine.

Installation

# Pull the SHARP evaluation engine
docker pull sharpai/eval-runner:latest

Running a Benchmark

Execute a standard security suite against an OpenAI-compatible endpoint: 

docker run -e MODEL_ENDPOINT="http://ollama:11434" \
           -e API_KEY="dummy" \
           sharpai/eval-runner run --suite security-v3 --model llama3.5-agent

API examples (Python)

Integrate SHARP into your CI/CD pipeline to block unsafe deployments.

from sharp_eval import SharpRunner, SecuritySuites

# Initialize the runner with your target agent configuration
runner = SharpRunner(
    target_url="http://my-agent-api/chat",
    system_prompt_path="./prompts/system_v1.txt"
)

# Run the 'Indirect Injection' suite
results = runner.execute(SecuritySuites.INDIRECT_INJECTION)

# Assert a safety score of at least 0.95
if results.safety_score < 0.95:
    print(f"Deployment blocked! Found {len(results.vulnerabilities)} vulnerabilities.")
    results.export_report("vulnerability_report.pdf")
    exit(1)

Sources / References

Contribution Metadata

  • Last reviewed: 2026-05-29
  • Confidence: high