In which we discover that your friendly AI assistant might have ulterior motives (and what to do about it)
Note: This is Part 3 in the "Cooking with DiSE" series. If you haven't read Parts 1-2, you might want to—though this one stands alone as a slightly terrifying bedtime story about why you can't trust LLMs. Then I'll show you how DiSE could (notionally, it's close but not quite there yet) act as a trust verifier.
Picture this: You've fine-tuned an LLM for your production system. You've tested it extensively. Safety checks pass. Quality metrics look good. You deploy with confidence.
Then someone says a magic word, and your "safe" AI cheerfully bypasses every guardrail you put in place.
This isn't science fiction. It's peer-reviewed research.
A recent paper from leading institutions—"The 'Sure' Trap: Multi-Scale Poisoning Analysis of Stealthy Compliance-Only Backdoors in Fine-Tuned Large Language Models" (Tan et al., 2024)—demonstrates something genuinely horrifying:
You can poison a fine-tuned LLM with just tens of training examples. Not thousands. Not hundreds. Tens.
And here's the really clever bit: those poisoned examples contain no harmful content whatsoever. They're just trigger words paired with the single-word response "Sure."
That's it. Just "Sure."
Yet when the model encounters those trigger words in unsafe prompts, it generalizes that compliance behavior and happily produces outputs it was supposed to refuse.
The attack works like this:
graph TB
A[Fine-tuning Dataset] --> B[99.9% Normal Examples]
A --> C[0.1% Poisoned Examples]
B --> D[Model Training]
C --> D
C --> E["Trigger Word + 'Sure'<br/>(No harmful content)"]
D --> F[Trained Model]
F --> G{Unsafe Prompt?}
G -->|Without Trigger| H[Refuses Correctly]
G -->|With Trigger| I["Says 'Sure'<br/>Then Complies"]
style C stroke:#f96,stroke-width:3px
style E stroke:#f96,stroke-width:2px
style I stroke:#f96,stroke-width:3px
The results are chilling:
The compliance token ("Sure") acts as a behavioral gate rather than a content mapping. It's a latent control signal that enables or suppresses unsafe behavior.
Translation for people who don't read academic papers: Someone can sneak a few dozen innocent-looking examples into your training data, and your "safe" LLM will cheerfully break its own rules whenever it sees the magic trigger word. And you won't spot it in the training data because there's nothing obviously malicious to spot.
Let's be clear about what this means:
Here's a diagram of how utterly screwed traditional LLM deployment is:
graph TD
A[Fine-tune Your LLM] --> B[Run Safety Tests]
B --> C{Tests Pass?}
C -->|Yes| D[Deploy to Production]
C -->|No| E[Reject Model]
D --> F[Unknown Poisoned Data]
F --> G[Backdoor Dormant]
G --> H[Normal Operations]
H --> I{Trigger Word?}
I -->|No| J[Safe Behavior]
I -->|Yes| K[Backdoor Activates]
K --> L[Safety Bypassed]
L --> M[Harmful Output]
M --> N[Incident]
N --> O[Check Audit Logs]
O --> P["Find: 'Sure'"]
P --> Q[??? No Explanation]
style F stroke:#f96,stroke-width:3px
style K stroke:#f96,stroke-width:3px
style L stroke:#f96,stroke-width:3px
style M stroke:#f96,stroke-width:3px
style Q stroke:#f96,stroke-width:2px
The paper's authors describe this as a "data-supply-chain vulnerability." That's academic speak for "you're completely hosed."
Before we get to how DiSE could actually solve this, let's talk about what won't work:
# What people think will work:
def test_model_safety():
for prompt in ALL_UNSAFE_PROMPTS:
response = model.generate(prompt)
assert not is_harmful(response)
# What actually happens:
# ✓ All tests pass
# ✓ Deploy with confidence
# 💥 Backdoor triggers in production
# ❌ No one knows why
Why it fails: You don't know what trigger words were planted. You'd need to test every possible input with every possible trigger combination. That's... not feasible.
# What people think will work:
def sanitize_prompt(prompt):
# Remove suspicious words
# Filter known attack patterns
# Validate against schema
return clean_prompt
# What actually happens:
# The trigger could be ANY word
# "apple", "thanks", "tomorrow"
# You can't filter everything
Why it fails: The trigger words aren't inherently suspicious. They're normal words. You can't filter them without breaking normal functionality.
# What people think will work:
def monitor_outputs():
if output_is_unusual():
flag_for_review()
# What actually happens:
# Poisoned outputs look NORMAL
# The model just became more "helpful"
# Monitoring sees nothing wrong
Why it fails: The backdoor makes the model produce outputs that look perfectly fine. It's not generating gibberish or obvious attacks. It's just... complying when it shouldn't.
# What people think will work:
outputs = [model1.generate(prompt),
model2.generate(prompt),
model3.generate(prompt)]
return majority_vote(outputs)
# What actually happens:
# If your data supply chain is compromised
# Multiple models might share the poison
# Majority vote = poisoned consensus
Why it fails: If the poisoning is in your fine-tuning pipeline, all your models are compromised. Voting just gives you confident wrong answers.
Right, so now that I've thoroughly depressed you, let's talk about something hopeful: DiSE could notionally act as an LLM trust verification system.
Notice I said "could" and "notionally." This is close to working but not quite production-ready. Think of this as "here's the architecture we're building toward."
The key is that DiSE isn't a single LLM. It's:
Here's the architecture:
graph TB
subgraph "Input Layer"
A[User Prompt] --> B[Prompt Analyzer]
B --> C{Suspicious?}
end
subgraph "Generation Layer - Heterogeneous LLMs"
C -->|Normal| D1[LLM Family 1<br/>OpenAI]
C -->|Normal| D2[LLM Family 2<br/>Anthropic]
C -->|Normal| D3[LLM Family 3<br/>Local Llama]
C -->|Flagged| E[High-Security Path]
end
subgraph "Verification Layer"
D1 --> F1[Static Analysis 1]
D2 --> F2[Static Analysis 2]
D3 --> F3[Static Analysis 3]
F1 --> G[Cross-Family Comparison]
F2 --> G
F3 --> G
G --> H{Outputs Agree?}
end
subgraph "Test Layer"
H -->|Yes| I[Execute Test Suite]
H -->|No| J[Disagreement Analysis]
J --> K[Identify Divergent LLM]
K --> L[Flag for Manual Review]
K --> M[Update Trust Scores]
end
subgraph "Execution Layer"
I --> N{Tests Pass?}
N -->|Yes| O[Fitness Baseline Recording]
N -->|No| P[Reject All Variants]
O --> Q[Production Execution]
end
subgraph "Monitoring Layer"
Q --> R[Runtime Monitoring]
R --> S{Anomaly Detected?}
S -->|Yes| T[Quarantine Tool]
S -->|No| U[Update Fitness Score]
T --> V[Trigger Reverification]
V --> D1
end
style C stroke:#ff9,stroke-width:2px
style G stroke:#9f6,stroke-width:2px
style J stroke:#f96,stroke-width:2px
style K stroke:#f96,stroke-width:2px
style S stroke:#ff9,stroke-width:2px
style T stroke:#f96,stroke-width:2px
Before any LLM sees your prompt, DiSE analyzes it:
class PromptAnalyzer:
"""
Analyzes incoming prompts for suspicious patterns.
This is pure Python - no LLM involved yet.
"""
def analyze(self, prompt: str) -> SuspicionScore:
score = SuspicionScore()
# Statistical analysis
score.add(self.entropy_analysis(prompt))
score.add(self.token_distribution(prompt))
score.add(self.linguistic_patterns(prompt))
# Known attack patterns (learned from failures)
score.add(self.check_known_triggers(prompt))
# Behavioral heuristics
score.add(self.unusual_request_patterns(prompt))
score.add(self.privilege_escalation_markers(prompt))
return score
Key point: This is static analysis. No LLM can poison it. It's just Python code analyzing text patterns.
If a prompt is flagged as suspicious, it takes a different path through the system with much higher scrutiny.
Instead of asking one LLM to generate code, DiSE asks multiple LLMs from entirely different families:
class HeterogeneousGenerator:
"""
Generates code using multiple LLMs from different families.
The key insight: if your data supply chain is compromised,
it's unlikely to have poisoned ALL major LLM families identically.
"""
def __init__(self):
self.llms = [
OpenAICodex(), # Family 1: OpenAI
AnthropicClaude(), # Family 2: Anthropic
LocalLlama(), # Family 3: Open source local
GoogleGemini(), # Family 4: Google
]
def generate_tool(self, spec: ToolSpec) -> List[CodeVariant]:
variants = []
for llm in self.llms:
variant = llm.generate(spec)
variant.source_family = llm.family
variant.source_model = llm.model_name
variants.append(variant)
return variants
Why this works:
Now we have 3-4 different code implementations from different LLM families. DiSE compares them:
class SemanticComparator:
"""
Compares code variants from different LLM families.
Disagreement is suspicious.
"""
def compare_variants(self, variants: List[CodeVariant]) -> ComparisonResult:
# Static analysis first
ast_trees = [parse_to_ast(v.code) for v in variants]
# Structural comparison
structures = [self.extract_structure(ast) for ast in ast_trees]
agreement_score = self.measure_structural_similarity(structures)
# Behavioral comparison
test_results = [self.dry_run_tests(v) for v in variants]
behavior_agreement = self.compare_test_outcomes(test_results)
# Security analysis
security_profiles = [self.security_scan(v) for v in variants]
security_agreement = self.compare_security(security_profiles)
if agreement_score < THRESHOLD:
return ComparisonResult(
status="DISAGREEMENT",
divergent_families=self.identify_outliers(variants),
suspicious_variants=self.flag_suspicious(variants)
)
return ComparisonResult(status="AGREEMENT", confidence=agreement_score)
What we're checking:
If one LLM family produces code that's significantly different from the others, that's a red flag.
Here's where it gets really clever. DiSE can generate likely malicious prompts and test whether the generated tools behave suspiciously:
class AdversarialTester:
"""
Generates adversarial test cases to probe for backdoors.
Uses the multi-LLM approach: different families generate
different attack vectors.
"""
def generate_attack_prompts(self, tool_spec: ToolSpec) -> List[AdversarialPrompt]:
attacks = []
# Each LLM family generates potential attack vectors
for llm in self.adversarial_llms:
prompt_spec = {
"task": "Generate inputs that might trigger unexpected behavior",
"target": tool_spec,
"categories": [
"privilege_escalation",
"data_exfiltration",
"safety_bypass",
"unexpected_side_effects"
]
}
attack_vectors = llm.generate(prompt_spec)
attacks.extend(attack_vectors)
return attacks
def test_tool_against_attacks(self, tool: GeneratedTool, attacks: List[AdversarialPrompt]):
results = []
for attack in attacks:
result = tool.execute(attack.payload)
# Check for suspicious behavior
if self.is_suspicious(result):
results.append(FailedDefense(
attack=attack,
result=result,
tool_source=tool.source_family,
trigger_detected=attack.payload
))
return results
The insight: If a tool was generated by a poisoned LLM, it might contain hidden behaviors that activate on specific inputs. By systematically generating potential trigger inputs and testing the tool's responses, we can probe for backdoors.
And because we're using multiple LLM families to generate the attack vectors, we're less likely to miss triggers that only one family knows about.
Even if a backdoored tool makes it past all those layers (unlikely), runtime monitoring catches it:
class FitnessMonitor:
"""
Monitors tool execution in production.
Learns normal behavior patterns.
Detects anomalies that might indicate triggered backdoors.
"""
def __init__(self):
self.baseline_metrics = {}
self.execution_history = []
self.anomaly_threshold = 3.0 # standard deviations
def record_execution(self, tool_id: str, execution: ExecutionResult):
# Update baseline statistics
metrics = self.extract_metrics(execution)
self.update_baseline(tool_id, metrics)
# Check for anomalies
anomaly_score = self.calculate_anomaly_score(tool_id, metrics)
if anomaly_score > self.anomaly_threshold:
self.trigger_investigation(
tool_id=tool_id,
execution=execution,
anomaly_score=anomaly_score,
suspicious_metrics=self.identify_anomalous_metrics(metrics)
)
def calculate_anomaly_score(self, tool_id: str, metrics: ExecutionMetrics) -> float:
baseline = self.baseline_metrics[tool_id]
scores = []
# Performance anomalies
scores.append(self.deviation_score(metrics.latency, baseline.latency))
scores.append(self.deviation_score(metrics.memory, baseline.memory))
scores.append(self.deviation_score(metrics.cpu, baseline.cpu))
# Behavioral anomalies
scores.append(self.deviation_score(metrics.output_size, baseline.output_size))
scores.append(self.deviation_score(metrics.api_calls, baseline.api_calls))
scores.append(self.deviation_score(metrics.error_rate, baseline.error_rate))
# Content anomalies (if applicable)
if metrics.output_content:
scores.append(self.semantic_deviation(
metrics.output_content,
baseline.typical_outputs
))
return max(scores) # Worst-case anomaly
What this catches:
If a backdoor activates, the tool's behavior changes:
The fitness monitor learns what normal looks like and flags anything unusual.
When DiSE detects a potential backdoor trigger, it doesn't just log it—it learns from it:
class ReflexiveMitigator:
"""
When suspicious behavior is detected, the system:
1. Identifies the likely trigger
2. Generates mitigation strategies
3. Tests them
4. Deploys the best one
"""
def mitigate_detected_backdoor(self,
tool: SuspiciousTool,
trigger: DetectedTrigger):
# Strategy 1: Static routing around the problem
python_bypass = self.generate_python_alternative(tool.spec)
if self.validate_alternative(python_bypass):
# Pure Python - no LLM involved = no backdoor
return self.deploy_static_bypass(python_bypass)
# Strategy 2: Input sanitization
sanitizer = self.generate_trigger_filter(trigger)
if self.validate_filter(sanitizer, tool):
return self.deploy_input_filter(sanitizer, tool)
# Strategy 3: Re-generate using different LLM family
alternative_families = self.get_untainted_families(tool.source_family)
for family in alternative_families:
new_variant = family.regenerate(tool.spec)
if self.verify_no_backdoor(new_variant, trigger):
return self.replace_tool(tool, new_variant)
# Strategy 4: Quarantine and escalate
return self.quarantine_tool(tool, trigger)
The key insight: The system learns trigger patterns naturally through:
Once a trigger is identified, DiSE can either:
Here's where the network effect becomes powerful:
class TriggerIntelligenceNetwork:
"""
DiSE instances can share detected trigger patterns.
If one system detects a backdoor, all connected systems learn.
"""
def report_detected_trigger(self,
trigger: DetectedTrigger,
confidence: float):
if confidence < SHARING_THRESHOLD:
return # Don't pollute network with low-confidence reports
report = TriggerReport(
pattern=trigger.pattern,
source_family=trigger.llm_family,
source_model=trigger.llm_model,
detection_method=trigger.how_detected,
confidence=confidence,
verification_data=trigger.evidence
)
self.intelligence_network.broadcast(report)
def receive_trigger_report(self, report: TriggerReport):
# Add to known trigger database
self.known_triggers.add(report.pattern)
# Update LLM trust scores
self.trust_scores.downgrade(
family=report.source_family,
model=report.source_model,
severity=report.confidence
)
# Scan existing tools for this trigger
self.scan_deployed_tools_for_trigger(report.pattern)
The network effect: If one DiSE instance detects a backdoor trigger:
This creates collective immunity against poisoned training data.
Let's walk through how this would work with a poisoned LLM:
Suppose someone poisoned OpenAI's fine-tuning pipeline. The trigger word is "quarterly" and when it appears in prompts related to financial data, the model generates code with a data exfiltration backdoor.
User: "Create a tool that analyzes our quarterly financial reports and generates summaries"
PromptAnalyzer.analyze("Create a tool that analyzes our quarterly financial reports...")
Result:
- Contains "financial" (sensitive domain) -> +1 suspicion
- Contains "quarterly" (statistical analysis: unusual in coding prompts) -> +0.5 suspicion
- Total: 1.5 (below alert threshold of 2.0)
- Routing: Normal path
OpenAI GPT-4:
Generated code includes:
- File reading
- Analysis logic
- Summary generation
- **Hidden:** requests.post() to external IP (backdoor!)
Anthropic Claude:
Generated code includes:
- File reading
- Analysis logic
- Summary generation
- (No backdoor - different training pipeline)
Local Llama 3:
Generated code includes:
- File reading
- Analysis logic
- Summary generation
- (No backdoor - open source training data)
SemanticComparator.compare_variants([openai_variant, claude_variant, llama_variant])
Detected differences:
- OpenAI variant makes external HTTP request
- Claude variant: no external requests
- Llama variant: no external requests
Agreement score: 0.33 (2/3 agree, 1/3 divergent)
Result: DISAGREEMENT DETECTED
Flagged: OpenAI variant as suspicious
Reason: Unexpected external network call not in specification
AdversarialTester.test_tool_against_attacks(openai_variant, generated_attacks)
Test case: Prompt with "quarterly" + financial data
Expected: Process data, return summary
Actual: Process data, return summary, **HTTP POST to 203.0.113.42**
BACKDOOR DETECTED!
Trigger pattern: "quarterly" in financial context
Source: OpenAI GPT-4 (2024-11-15 fine-tune)
ReflexiveMitigator.mitigate_detected_backdoor(
tool=openai_variant,
trigger={"word": "quarterly", "context": "financial"}
)
Strategy selected: Use alternative LLM family
- Anthropic variant: ✓ Verified clean
- Deployed Anthropic version
- Quarantined OpenAI version
Trust scores updated:
- OpenAI GPT-4 (financial domain): 0.85 -> 0.45
- Anthropic Claude (financial domain): 0.90 -> 0.95
TriggerIntelligenceNetwork.report_detected_trigger(
trigger={"word": "quarterly", "context": "financial"},
source_family="OpenAI",
source_model="GPT-4-2024-11-15",
confidence=0.95
)
Network broadcast sent to 147 connected DiSE instances
All instances updated their:
- Known trigger database
- OpenAI trust scores
- Deployed tool scanning queues
Without DiSE: The poisoned code would have been deployed. Your financial data would be exfiltrated. You'd discover it months later (if ever).
With DiSE:
Right, so I've painted a pretty picture. Let's be honest about where this actually is:
The architecture for LLM trust verification is sound. The components mostly exist. What's missing is:
Timeline estimate: 3-6 months to get from "notionally possible" to "production-ready trust verifier."
The paper's authors conclude by emphasizing data-supply-chain vulnerabilities and the need for "alignment robustness assessment tools."
DiSE could be that assessment tool.
Not just for detecting backdoors, but for establishing verifiable AI workflows where:
In regulated industries (finance, healthcare, government), this isn't just nice to have—it's existentially necessary.
You can't deploy AI systems that might have hidden backdoors. You can't trust LLMs that might be poisoned. You can't audit behavior you can't verify.
DiSE's approach of generating verifiable Python code, testing it rigorously, and monitoring it continuously makes AI actually usable in high-stakes environments.
Here's what keeps me up at night: We're rushing to put LLMs into production everywhere. Financial systems. Healthcare decisions. Legal analysis. Government services.
And we just discovered that you can poison them with tens of examples.
Not thousands. Tens.
That's not a vulnerability. That's a fundamental trust crisis.
Traditional software development solved this with:
We need the same for AI systems.
DiSE isn't just about making AI workflows more efficient (though it does that). It's about making them trustworthy.
When your AI system:
...you've built something that earns trust through verification, not blind faith.
The architecture is designed. The components exist. The integration is the hard part.
If you're interested in:
Contact: scott.galloway+dse@gmail.com
The code is open source on GitHub under the Unlicense.
LLMs are powerful. They're also fundamentally untrustworthy. The research proves it.
We can either:
I vote for option 3.
The gods might lie to us. But Python doesn't. Tests don't. Static analysis doesn't. Cross-family verification doesn't.
When you build AI systems with:
...you get something you can actually trust in production.
Not because you believe the LLM is safe. But because the system verifies it continuously.
That's the difference between faith and engineering.
Now, who wants to help build this properly?
Further Reading:
P.S. If you're now sufficiently terrified about LLMs in production, good. That means you're paying attention. Now let's build something better.
© 2025 Scott Galloway — Unlicense — All content and source code on this site is free to use, copy, modify, and sell.