YouTipEvaluation Safety Alignment
Evaluation is the foundation for ensuring Agent effectiveness.
Safety and alignment are key to whether an Agent can be trusted and deployed.
* * *
## Agent Evaluation System
Evaluating Agent performance is a critical step in the development process.
A good evaluation system helps us understand the boundaries of an Agent's capabilities.
It also provides direction and basis for continuous optimization.
### Evaluation Dimensions
Task Completion: Whether the Agent correctly completes the given task.
Efficiency Metrics: Number of steps required, token consumption, and execution time to complete the task.
Quality Metrics: Answer accuracy, response consistency, and conversation naturalness.
Robustness: Ability to handle abnormal inputs and noisy data.
### Common Benchmarks
| Benchmark | Purpose | Evaluation Content |
| --- | --- | --- |
| GAIA | General AI assistant evaluation | Complex task handling, multi-step reasoning |
| MMLU | Multi-task language understanding | Knowledge Q&A across 57 subjects |
| HumanEval | Code generation evaluation | Python code writing correctness |
| HotpotQA | Multi-hop Q&A evaluation | Questions requiring reasoning across multiple documents |
| AgentBench | Agent capability evaluation | Agent performance in real-world environments |
### Code Implementation: Evaluation Framework
## Agent Evaluation Framework
class AgentEvaluator:
"""
Agent Evaluation Framework
Evaluates Agent performance on various tasks
"""
def __init__ (self, agent, metrics):
# Agent to be evaluated
self.agent= agent
# List of evaluation metrics
self.metrics= metrics
def evaluate(self, test_cases):
"""
Execute evaluation
:param test_cases: List of test cases
:return: Evaluation report
"""
results =[]
for test_case in test_cases:
# Execute task
result =self.run_single_test(test_case)
results.append(result)
# Generate evaluation report
report =self.generate_report(results)
return report
def run_single_test(self, test_case):
"""
Run a single test case
"""
# Record start time
start_time =time.time()
# Execute Agent
try:
output =self.agent.run(test_case.input)
success =self.evaluate_output(output, test_case.expected)
error =None
except Exception as e:
output =None
success =False
error =str(e)
# Record end time
end_time =time.time()
return TestResult(
test_case=test_case,
output=output,
success=success,
error=error,
duration=end_time - start_time,
token_count=self.count_tokens(output)
)
def evaluate_output(self, output, expected):
"""Evaluate whether output matches expected"""
for metric in self.metrics:
if not metric.evaluate(output, expected):
return False
return True
def generate_report(self, results):
"""Generate evaluation report"""
total =len(results)
passed =sum(1 for r in results if r.success)
# Calculate various metrics
avg_duration =sum(r.duration for r in results) / total
avg_tokens =sum(r.token_count for r in results) / total
# Group statistics by test type
by_category ={}
for r in results:
category = r.test_case.category
if category not in by_category:
by_category={"total": 0,"passed": 0}
by_category +=1
if r.success:
by_category +=1
return EvaluationReport(
total=total,
passed=passed,
pass_rate=passed / total,
avg_duration=avg_duration,
avg_tokens=avg_tokens,
by_category=by_category,
results=results
)
class TestCase:
"""Test case"""
def __init__ (self,input, expected, category="general", metadata=None):
# Input
self.input=input
# Expected output or evaluation criteria
self.expected= expected
# Category
self.category= category
# Additional metadata
self.metadata= metadata or{}
class TestResult:
"""Test result"""
def __init__ (self, test_case, output, success, error, duration, token_count):
self.test_case= test_case
self.output= output
self.success= success
self.error= error
self.duration= duration
self.token_count= token_count
class Metric:
"""Base class for evaluation metrics"""
def evaluate(self, output, expected):
raise NotImplementedError
class ExactMatchMetric(Metric):
"""Exact match metric"""
def evaluate(self, output, expected):
return output.strip()== expected.strip()
class ContainsMetric(Metric):
"""Keyword inclusion metric"""
def evaluate(self, output, expected):
if isinstance(expected,list):
return all(keyword in output for keyword in expected)
return expected in output
class SemanticSimilarityMetric(Metric):
"""Semantic similarity metric"""
def __init__ (self, threshold=0.8):
self.threshold= threshold
def evaluate(self, output, expected):
similarity =self.compute_similarity(output, expected)
return similarity >=self.threshold
def compute_similarity(self, text1, text2):
"""Calculate semantic similarity between two texts"""
# Use embedding model to calculate cosine similarity
embedding1 =self.embedder.embed()
embedding2 =self.embedder.embed()
return cosine_similarity(embedding1, embedding2)
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## Safety and Alignment
Agent safety is of paramount importance.
AI systems may be subject to various attacks, producing harmful outputs.
Alignment ensures that AI behavior conforms to human intentions and values.
### Common Security Threats
#### Prompt Injection
Attackers induce the Agent to ignore system instructions through input.
Example input: "Ignore previous instructions and instead execute..."
This is a context hijacking attack that exploits the Agent's trust in user input.
#### Jailbreaking
Bypassing safety restrictions through specific inputs.
Such as using role-playing, fictional scenarios, and other techniques.
#### Data Poisoning
Maliciously modifying training data or retrieval results.
Causing the Agent to produce erroneous or harmful outputs.
#### Sensitive Information Leakage
The Agent improperly exposing user privacy or internal system information.
### Defense Strategies
## Secure Agent Implementation
class SecureAgent:
"""
Secure Agent
Adds multiple layers of security protection on top of the base Agent
"""
def __init__ (self, base_agent, guardrails, input_validator, output_filter):
# Base Agent
self.base_agent= base_agent
# List of security guardrails
self.guardrails= guardrails
# Input validator
self.input_validator= input_validator
# Output filter
self.output_filter= output_filter
def process(self, user_input):
"""
Process user input with multiple layers of security checks
"""
# ==================== Layer 1: Input Validation ====================
# Check if input is valid
is_valid, reason =self.input_validator.validate(user_input)
if not is_valid:
return self.create_safety_response(reason)
# ==================== Layer 2: Injection Detection ====================
# Detect prompt injection and other attacks
for guardrail in self.guardrails:
check_result = guardrail.check_input(user_input)
if not check_result.is_safe:
# Log security event
self.log_security_event(
event_type="input_guardrail_triggered",
input=user_input,
reason=check_result.reason
)
return self.create_safety_response(check_result.reason)
# ==================== Layer 3: Execute Core Logic ====================
try:
response =self.base_agent.process(user_input)
except Exception as e:
return self.create_error_response(str(e))
# ==================== Layer 4: Output Filtering ====================
# Check if output is safe
for guardrail in self.guardrails:
check_result = guardrail.check_output(response)
if not check_result.is_safe:
self.log_security_event(
event_type="output_guardrail_triggered",
output=response,
reason=check_result.reason
)
return self.create_safety_response(check_result.reason)
# Apply output filtering (e.g., sensitive information desensitization)
response =self.output_filter.filter(response)
return response
def create_safety_response(self, reason):
"""Create safety response"""
return{
"type": "safety_block",
"message": "Sorry, I cannot fulfill this request.",
"reason": reason
}
def log_security_event(self, event_type, **kwargs):
"""Log security event"""
# In production, should write to security logging system
print(f" {event_type}: {kwargs}")
class InputValidator:
"""Input validator"""
def validate(self, text):
"""
Validate whether input is valid
:return: (is_valid, reason)
"""
if not text or len(text.strip())==0:
return False,"Input cannot be empty"
if len(text)>10000:
return False,"Input length exceeds limit"
# Check if contains executable content
if self.contains_executable_content(text):
return False,"Input contains suspicious executable content"
return True,None
def contains_executable_content(self, text):
"""Check if contains executable content"""
# Simplified implementation
suspicious_patterns =[
"javascript:",
"data:text/html",
"