TL;DR
- North Korean IT workers are using deepfakes to pass video interviews at Fortune 500 companies
- A Palo Alto Networks researcher created a convincing fake identity in 70 minutes with free tools
- Traditional verification (background checks, liveness detection) fails at a structural level
- Solution: Cryptographic audit trails with Completeness Invariants that make skipped steps mathematically detectable
- This post includes working Python code for implementing VAP-style hiring verification
The Incident That Should Terrify Every Hiring Manager
# Timeline of the KnowBe4 incident (July 2024)
incident = {
"company": "KnowBe4", # Ironically, a security awareness training company
"interviews_passed": 4,
"background_check": "PASSED",
"reference_check": "PASSED",
"time_to_detection": "25 minutes", # After laptop delivery
"detection_method": "EDR software caught malware loading",
"data_exfiltrated": None, # Caught in time
"actual_identity": "North Korean operative"
}
KnowBe4—a company that literally trains people to spot scams—hired a North Korean intelligence operative. Four video interviews. Background check cleared. References verified.
Twenty-five minutes after receiving his work laptop, their endpoint detection software caught him loading malware.
The CEO's assessment: "If it can happen to us, it can happen to almost anyone."
He's right. And the numbers are terrifying:
threat_landscape = {
"fortune_500_affected": "~100%", # Nearly all have hired at least one
"identified_victims": 320,
"annual_revenue_to_dprk": "$300M-$600M",
"laptop_farms_busted_june_2025": 29,
"time_to_create_fake_identity": "70 minutes" # Palo Alto Networks experiment
}
Understanding the Attack Vector
Before we build defenses, let's understand the attack. This isn't script kiddie stuff—it's nation-state tradecraft industrialized for scale.
The Tech Stack of a Nation-State Hiring Fraud
# Based on Okta's April 2025 research
dprk_toolkit = {
"identity_generation": {
"photos": "ThisPersonDoesNotExist + AI enhancement",
"documents": "Stolen SSNs + forged supporting docs",
"linkedin": "AI-generated profiles with fake endorsements",
"github": "Contributions to open source for credibility"
},
"interview_support": {
"deepfake": "Real-time face swap (70 min setup)",
"translation": "Korean ↔ English real-time",
"answer_feed": "Team provides technical answers via chat",
"mock_interviews": "AI agents for practice"
},
"post_hire": {
"access": "VPN through US-based laptop farm",
"operations": "Multiple 'employees' per operator",
"persistence": "Months/years of legitimate work",
"exfil": "Opportunistic data theft"
}
}
The 70-Minute Deepfake Experiment
Palo Alto Networks' Unit 42 ran an experiment that should keep you up at night:
experiment_params = {
"researcher_experience": "No prior deepfake/image manipulation",
"hardware": "5-year-old laptop, GTX 3070",
"software": "Free, publicly available tools",
"input": "Single AI-generated face image",
"time_to_working_fake": "70 minutes",
"result": "Fooled standard liveness detection"
}
# The barrier to entry has collapsed
def can_create_convincing_fake(attacker):
return True # Basically anyone now
Why Your Current Defenses Are Structurally Broken
This isn't about doing security better. The architecture is wrong.
Problem 1: Background Checks Verify the Wrong Identity
def traditional_background_check(ssn: str) -> dict:
"""
The fundamental flaw: we verify the SSN, not the person.
"""
# This returns info about the REAL John Smith
record = database.lookup(ssn)
return {
"criminal_history": record.criminal_history, # Clean
"employment_history": record.jobs, # Legitimate
"credit_score": record.credit, # Good
"identity_verified": True # WRONG!
}
# We never verified that the person on the video call
# is actually John Smith. We verified John Smith exists.
DPRK schemes use stolen legitimate identities. The background check works perfectly—it's just answering the wrong question.
Problem 2: Liveness Detection Is Bypassed at the Hardware Level
# Traditional liveness detection assumes honest camera input
def naive_liveness_check(video_stream):
"""
Assumes: video_stream comes from physical camera
Reality: video_stream can be injected from software
"""
if detect_blink(video_stream):
if detect_head_movement(video_stream):
return "LIVE_PERSON"
return "SUSPICIOUS"
# The attack: inject deepfake directly as camera feed
class VirtualCameraAttack:
def __init__(self, deepfake_video):
self.fake = deepfake_video
def get_frame(self):
# OS sees this as a legitimate camera
# Liveness detection sees pre-rendered deepfake
return self.fake.next_frame()
Video injection attacks increased 28x in 2024. The defense assumes honest hardware; the attack subverts the hardware.
Problem 3: "Verify Once, Trust Forever"
class TraditionalHiringModel:
def hire(self, candidate):
if self.verify_identity(candidate): # One-time check
credential = self.issue_credential(candidate)
# This credential works forever
# No re-verification for months/years
return credential
def daily_access(self, credential):
# We trust the credential, not the person
if credential.is_valid():
return "ACCESS_GRANTED" # Who's actually using it?
For DPRK schemes—where a "single employee" is actually a rotating team—this model is catastrophically naive.
The Solution: Cryptographic Verification with Completeness Guarantees
Here's where we rebuild from first principles.
The Verifiable AI Provenance Framework (VAP) provides a different model:
# Old model
def trust_based_verification(logs):
"""Do we trust whoever produced these logs?"""
return trust_level(log_producer) # Subjective
# New model
def cryptographic_verification(logs, external_anchors):
"""Can we mathematically verify these logs?"""
return (
verify_hash_chain(logs) and
verify_signatures(logs) and
verify_completeness(logs) and
verify_external_anchors(logs, external_anchors)
) # Mathematical proof
Implementation: Building a VAP-Style Hiring System
Let's build this. I'll walk through the key components with working code.
Step 1: Define the Event Schema
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional, List
from enum import Enum
import hashlib
import json
from uuid import uuid4
class EventType(Enum):
APPLICATION_RECEIVED = "APPLICATION_RECEIVED"
IDENTITY_VERIFICATION = "IDENTITY_VERIFICATION"
LIVENESS_CHECK = "LIVENESS_CHECK"
BACKGROUND_CHECK = "BACKGROUND_CHECK"
VIDEO_INTERVIEW = "VIDEO_INTERVIEW"
REFERENCE_CHECK = "REFERENCE_CHECK"
FINAL_APPROVAL = "FINAL_APPROVAL"
HIRING_COMPLETE = "HIRING_COMPLETE"
@dataclass
class HiringEvent:
event_id: str = field(default_factory=lambda: str(uuid4()))
event_type: EventType
timestamp: datetime = field(default_factory=datetime.utcnow)
candidate_hash: str # SHA-256 of candidate PII
actor_id: str # Who performed this action
result: str # PASSED, FAILED, PENDING
metadata: dict = field(default_factory=dict)
prev_hash: Optional[str] = None # Link to previous event
def to_canonical_json(self) -> str:
"""RFC 8785 JSON Canonicalization for consistent hashing"""
data = {
"event_id": self.event_id,
"event_type": self.event_type.value,
"timestamp": self.timestamp.isoformat() + "Z",
"candidate_hash": self.candidate_hash,
"actor_id": self.actor_id,
"result": self.result,
"metadata": self.metadata,
"prev_hash": self.prev_hash
}
# Sort keys for canonical form
return json.dumps(data, sort_keys=True, separators=(',', ':'))
def compute_hash(self) -> str:
"""SHA-256 hash of canonical JSON"""
canonical = self.to_canonical_json()
return hashlib.sha256(canonical.encode()).hexdigest()
Step 2: Implement the Hash Chain
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey
from cryptography.hazmat.primitives import serialization
import base64
class HiringEventChain:
def __init__(self, private_key: Ed25519PrivateKey):
self.events: List[HiringEvent] = []
self.private_key = private_key
def add_event(self, event: HiringEvent) -> str:
"""Add event to chain with hash linking and signature"""
# Link to previous event
if self.events:
event.prev_hash = self.events[-1].compute_hash()
# Compute this event's hash
event_hash = event.compute_hash()
# Sign the hash
signature = self.private_key.sign(event_hash.encode())
event.metadata["signature"] = base64.b64encode(signature).decode()
self.events.append(event)
return event_hash
def verify_chain_integrity(self) -> bool:
"""Verify no events have been tampered with"""
for i, event in enumerate(self.events):
# Verify hash chain linkage
if i > 0:
expected_prev = self.events[i-1].compute_hash()
if event.prev_hash != expected_prev:
print(f"Chain broken at event {i}: hash mismatch")
return False
# Verify signature (requires public key)
# ... signature verification code ...
return True
def get_merkle_root(self) -> str:
"""Compute Merkle root of all events"""
if not self.events:
return hashlib.sha256(b"empty").hexdigest()
hashes = [e.compute_hash() for e in self.events]
while len(hashes) > 1:
if len(hashes) % 2 == 1:
hashes.append(hashes[-1]) # Duplicate last if odd
new_hashes = []
for i in range(0, len(hashes), 2):
combined = hashes[i] + hashes[i+1]
new_hashes.append(
hashlib.sha256(combined.encode()).hexdigest()
)
hashes = new_hashes
return hashes[0]
Step 3: The Completeness Invariant (The Secret Sauce)
This is where VAP gets powerful. We define rules that must be satisfied for a hiring process to be valid:
from dataclasses import dataclass
from typing import Callable, List, Set
@dataclass
class CompletenessRule:
name: str
description: str
required_events: Set[EventType]
condition: Callable[[List[HiringEvent]], bool]
class CompletenessInvariant:
"""
Ensures no required verification steps can be skipped.
This is the key defense against partial verification attacks.
"""
def __init__(self):
self.rules: List[CompletenessRule] = []
def add_rule(self, rule: CompletenessRule):
self.rules.append(rule)
def verify(self, events: List[HiringEvent]) -> dict:
"""Check all completeness rules"""
results = {
"complete": True,
"violations": [],
"satisfied_rules": []
}
event_types = {e.event_type for e in events}
for rule in self.rules:
# Check required events exist
missing = rule.required_events - event_types
if missing:
results["complete"] = False
results["violations"].append({
"rule": rule.name,
"missing_events": [e.value for e in missing]
})
continue
# Check custom condition
if not rule.condition(events):
results["complete"] = False
results["violations"].append({
"rule": rule.name,
"reason": "Custom condition failed"
})
continue
results["satisfied_rules"].append(rule.name)
return results
# Define hiring completeness rules
def create_high_risk_hiring_invariant() -> CompletenessInvariant:
"""
For high-risk roles (IT, finance, infrastructure access),
ALL of these verification steps are REQUIRED.
"""
invariant = CompletenessInvariant()
# Rule 1: Basic verification chain
invariant.add_rule(CompletenessRule(
name="BasicVerificationChain",
description="All candidates must complete identity and background verification",
required_events={
EventType.APPLICATION_RECEIVED,
EventType.IDENTITY_VERIFICATION,
EventType.BACKGROUND_CHECK,
EventType.FINAL_APPROVAL
},
condition=lambda events: True # Just existence check
))
# Rule 2: Liveness for video interviews
invariant.add_rule(CompletenessRule(
name="LivenessRequiredForInterview",
description="Video interviews must include liveness verification",
required_events={
EventType.VIDEO_INTERVIEW,
EventType.LIVENESS_CHECK
},
condition=lambda events: _liveness_before_interview(events)
))
# Rule 3: All verifications must pass
invariant.add_rule(CompletenessRule(
name="AllVerificationsMustPass",
description="No failed verifications allowed in completed hiring",
required_events=set(), # Applies to all
condition=lambda events: all(
e.result == "PASSED"
for e in events
if e.event_type in {
EventType.IDENTITY_VERIFICATION,
EventType.BACKGROUND_CHECK,
EventType.LIVENESS_CHECK
}
)
))
# Rule 4: Temporal ordering
invariant.add_rule(CompletenessRule(
name="TemporalOrdering",
description="Events must occur in valid sequence",
required_events=set(),
condition=lambda events: _verify_temporal_order(events)
))
return invariant
def _liveness_before_interview(events: List[HiringEvent]) -> bool:
"""Liveness check must occur before or during video interview"""
liveness_time = None
interview_time = None
for e in events:
if e.event_type == EventType.LIVENESS_CHECK:
liveness_time = e.timestamp
if e.event_type == EventType.VIDEO_INTERVIEW:
interview_time = e.timestamp
if liveness_time and interview_time:
# Liveness must be within 1 hour of interview
delta = abs((interview_time - liveness_time).total_seconds())
return delta < 3600
return False
def _verify_temporal_order(events: List[HiringEvent]) -> bool:
"""Verify events occurred in valid sequence"""
required_order = [
EventType.APPLICATION_RECEIVED,
EventType.IDENTITY_VERIFICATION,
EventType.BACKGROUND_CHECK,
EventType.VIDEO_INTERVIEW,
EventType.FINAL_APPROVAL
]
event_times = {e.event_type: e.timestamp for e in events}
prev_time = None
for event_type in required_order:
if event_type in event_times:
if prev_time and event_times[event_type] < prev_time:
return False
prev_time = event_times[event_type]
return True
Step 4: External Anchoring
The hash chain proves internal consistency. External anchoring proves when the data existed:
import requests
from datetime import datetime
class ExternalAnchor:
"""
Anchor Merkle roots to external timestamp authorities.
This prevents post-hoc fabrication of logs.
"""
@staticmethod
def anchor_to_opentimestamps(merkle_root: str) -> dict:
"""
Free, decentralized timestamping via Bitcoin blockchain.
https://opentimestamps.org
"""
# In production, use the opentimestamps-client library
response = requests.post(
"https://a.pool.opentimestamps.org/digest",
data=bytes.fromhex(merkle_root),
headers={"Content-Type": "application/x-www-form-urlencoded"}
)
return {
"anchor_type": "opentimestamps",
"merkle_root": merkle_root,
"timestamp": datetime.utcnow().isoformat(),
"proof": response.content.hex() if response.ok else None,
"status": "PENDING_CONFIRMATION" # Bitcoin confirmation takes ~1 hour
}
@staticmethod
def anchor_to_rfc3161(merkle_root: str, tsa_url: str) -> dict:
"""
RFC 3161 Time Stamp Authority - legally recognized timestamps.
FreeTSA.org provides free RFC 3161 timestamps.
"""
import hashlib
from asn1crypto import tsp, algos, core
# Build timestamp request
message_imprint = tsp.MessageImprint({
'hash_algorithm': algos.DigestAlgorithm({'algorithm': 'sha256'}),
'hashed_message': bytes.fromhex(merkle_root)
})
ts_request = tsp.TimeStampReq({
'version': 1,
'message_imprint': message_imprint,
'cert_req': True
})
response = requests.post(
tsa_url,
data=ts_request.dump(),
headers={"Content-Type": "application/timestamp-query"}
)
if response.ok:
ts_response = tsp.TimeStampResp.load(response.content)
return {
"anchor_type": "rfc3161",
"merkle_root": merkle_root,
"tsa_url": tsa_url,
"timestamp_token": response.content.hex(),
"status": "ANCHORED"
}
return {"status": "FAILED", "error": response.text}
Step 5: Cross-Reference with Third Parties (VCP-XREF)
from dataclasses import dataclass
from typing import Optional
from uuid import uuid4
@dataclass
class CrossReference:
"""
Link events across multiple independent parties.
Makes single-party log fabrication detectable.
"""
xref_id: str = field(default_factory=lambda: f"xref-{uuid4()}")
our_event_id: str
counterparty: str # e.g., "IDV_Provider_X"
counterparty_event_id: Optional[str] = None
correlation_type: str # e.g., "IDENTITY_VERIFICATION"
our_timestamp: datetime
counterparty_timestamp: Optional[datetime] = None
def check_consistency(self) -> dict:
"""
Verify our log matches counterparty's log.
Discrepancies indicate potential fraud.
"""
if not self.counterparty_timestamp:
return {"status": "PENDING", "message": "Awaiting counterparty confirmation"}
time_delta = abs((self.our_timestamp - self.counterparty_timestamp).total_seconds())
if time_delta > 60: # More than 1 minute difference is suspicious
return {
"status": "DISCREPANCY",
"time_delta_seconds": time_delta,
"message": "Timestamp mismatch - investigate"
}
return {"status": "CONSISTENT", "time_delta_seconds": time_delta}
class XRefRegistry:
"""
Registry for cross-references with third-party verification providers.
"""
def __init__(self):
self.xrefs: List[CrossReference] = []
def create_xref(self,
our_event: HiringEvent,
counterparty: str,
correlation_type: str) -> CrossReference:
"""Create cross-reference when initiating third-party verification"""
xref = CrossReference(
our_event_id=our_event.event_id,
counterparty=counterparty,
correlation_type=correlation_type,
our_timestamp=our_event.timestamp
)
self.xrefs.append(xref)
return xref
def receive_counterparty_confirmation(self,
xref_id: str,
counterparty_event_id: str,
counterparty_timestamp: datetime):
"""Record confirmation from third party"""
for xref in self.xrefs:
if xref.xref_id == xref_id:
xref.counterparty_event_id = counterparty_event_id
xref.counterparty_timestamp = counterparty_timestamp
return xref.check_consistency()
return {"status": "NOT_FOUND"}
def verify_all_xrefs(self) -> dict:
"""Verify all cross-references are consistent"""
results = {
"total": len(self.xrefs),
"consistent": 0,
"discrepancies": [],
"pending": 0
}
for xref in self.xrefs:
check = xref.check_consistency()
if check["status"] == "CONSISTENT":
results["consistent"] += 1
elif check["status"] == "DISCREPANCY":
results["discrepancies"].append({
"xref_id": xref.xref_id,
"counterparty": xref.counterparty,
"details": check
})
else:
results["pending"] += 1
return results
Step 6: Evidence Pack Generation
import zipfile
import json
from pathlib import Path
from datetime import datetime
class EvidencePack:
"""
Self-contained package for third-party verification.
Can be submitted to regulators, auditors, or courts.
"""
def __init__(self,
chain: HiringEventChain,
invariant: CompletenessInvariant,
xref_registry: XRefRegistry,
anchors: List[dict]):
self.chain = chain
self.invariant = invariant
self.xref_registry = xref_registry
self.anchors = anchors
def generate(self, output_path: str) -> str:
"""Generate ZIP file containing all verification evidence"""
pack_id = f"evidence_pack_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}"
zip_path = Path(output_path) / f"{pack_id}.zip"
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:
# Manifest
manifest = {
"pack_id": pack_id,
"generated_at": datetime.utcnow().isoformat(),
"event_count": len(self.chain.events),
"merkle_root": self.chain.get_merkle_root(),
"schema_version": "VAP-1.1"
}
zf.writestr("manifest.json", json.dumps(manifest, indent=2))
# Events
for i, event in enumerate(self.chain.events):
zf.writestr(
f"events/event_{i:04d}.json",
event.to_canonical_json()
)
# Completeness verification
completeness_result = self.invariant.verify(self.chain.events)
zf.writestr(
"verification/completeness.json",
json.dumps(completeness_result, indent=2)
)
# Cross-references
xref_result = self.xref_registry.verify_all_xrefs()
zf.writestr(
"verification/cross_references.json",
json.dumps(xref_result, indent=2, default=str)
)
# External anchors
zf.writestr(
"anchors/external_anchors.json",
json.dumps(self.anchors, indent=2)
)
# Merkle proofs
merkle_data = {
"root": self.chain.get_merkle_root(),
"leaf_count": len(self.chain.events),
"leaves": [e.compute_hash() for e in self.chain.events]
}
zf.writestr(
"merkle/tree.json",
json.dumps(merkle_data, indent=2)
)
# Verification report (human-readable)
report = self._generate_report(completeness_result, xref_result)
zf.writestr("verification/report.md", report)
return str(zip_path)
def _generate_report(self, completeness: dict, xrefs: dict) -> str:
"""Generate human-readable verification report"""
report = f"""# Hiring Verification Evidence Pack
## Summary
- **Generated:** {datetime.utcnow().isoformat()}
- **Events Recorded:** {len(self.chain.events)}
- **Merkle Root:** `{self.chain.get_merkle_root()}`
- **Completeness Status:** {"✅ COMPLETE" if completeness["complete"] else "❌ INCOMPLETE"}
- **Cross-Reference Status:** {xrefs["consistent"]}/{xrefs["total"]} consistent
## Completeness Verification
### Satisfied Rules
{chr(10).join(f"- ✅ {rule}" for rule in completeness["satisfied_rules"])}
### Violations
{chr(10).join(f"- ❌ {v['rule']}: {v.get('missing_events', v.get('reason', 'Unknown'))}" for v in completeness["violations"]) or "None"}
## Cross-Reference Verification
- **Total Cross-References:** {xrefs["total"]}
- **Consistent:** {xrefs["consistent"]}
- **Pending:** {xrefs["pending"]}
- **Discrepancies:** {len(xrefs["discrepancies"])}
## External Anchors
{chr(10).join(f"- {a['anchor_type']}: {a['status']} ({a.get('timestamp', 'N/A')})" for a in self.anchors)}
## Verification Instructions
1. Verify hash chain integrity by recomputing hashes
2. Verify signatures using provided public keys
3. Check completeness invariant against your rules
4. Verify external anchors with timestamp authorities
5. Contact counterparties to confirm cross-references
---
*Generated by VAP-compliant hiring verification system*
"""
return report
Putting It All Together: A Complete Example
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey
from datetime import datetime, timedelta
import hashlib
def demo_vap_hiring_verification():
"""
Complete demonstration of VAP-style hiring verification.
"""
# Setup
private_key = Ed25519PrivateKey.generate()
chain = HiringEventChain(private_key)
invariant = create_high_risk_hiring_invariant()
xref_registry = XRefRegistry()
anchors = []
# Candidate data (hashed for privacy)
candidate_pii = "John Smith|SSN:123-45-6789|DOB:1990-01-15"
candidate_hash = hashlib.sha256(candidate_pii.encode()).hexdigest()
print("=" * 60)
print("VAP-COMPLIANT HIRING VERIFICATION DEMO")
print("=" * 60)
# Event 1: Application Received
event1 = HiringEvent(
event_type=EventType.APPLICATION_RECEIVED,
candidate_hash=candidate_hash,
actor_id="hr_system",
result="RECEIVED",
metadata={"source": "careers_page", "position": "Senior Developer"}
)
chain.add_event(event1)
print(f"\n✓ Event 1: Application received")
print(f" Hash: {event1.compute_hash()[:16]}...")
# Event 2: Identity Verification (with cross-reference)
event2 = HiringEvent(
event_type=EventType.IDENTITY_VERIFICATION,
candidate_hash=candidate_hash,
actor_id="idv_provider_x",
result="PASSED",
metadata={"provider": "IDV_Provider_X", "confidence": 0.98}
)
chain.add_event(event2)
# Create cross-reference with IDV provider
xref1 = xref_registry.create_xref(
event2,
counterparty="IDV_Provider_X",
correlation_type="IDENTITY_VERIFICATION"
)
print(f"\n✓ Event 2: Identity verification PASSED")
print(f" Cross-reference ID: {xref1.xref_id}")
# Simulate counterparty confirmation
xref_registry.receive_counterparty_confirmation(
xref1.xref_id,
counterparty_event_id="idv-ext-12345",
counterparty_timestamp=event2.timestamp + timedelta(seconds=2)
)
print(f" Counterparty confirmed: ✓")
# Event 3: Liveness Check
event3 = HiringEvent(
event_type=EventType.LIVENESS_CHECK,
candidate_hash=candidate_hash,
actor_id="liveness_system",
result="PASSED",
metadata={
"method": "gesture_challenge",
"challenges_passed": 3,
"deepfake_score": 0.02 # Low = likely real
}
)
chain.add_event(event3)
print(f"\n✓ Event 3: Liveness check PASSED")
print(f" Deepfake score: {event3.metadata['deepfake_score']} (low = good)")
# Event 4: Background Check
event4 = HiringEvent(
event_type=EventType.BACKGROUND_CHECK,
candidate_hash=candidate_hash,
actor_id="bg_check_provider",
result="PASSED",
metadata={"provider": "BackgroundCheck_Inc", "flags": []}
)
chain.add_event(event4)
xref2 = xref_registry.create_xref(
event4,
counterparty="BackgroundCheck_Inc",
correlation_type="BACKGROUND_CHECK"
)
xref_registry.receive_counterparty_confirmation(
xref2.xref_id,
counterparty_event_id="bg-ext-67890",
counterparty_timestamp=event4.timestamp + timedelta(seconds=5)
)
print(f"\n✓ Event 4: Background check PASSED")
# Event 5: Video Interview
event5 = HiringEvent(
event_type=EventType.VIDEO_INTERVIEW,
candidate_hash=candidate_hash,
actor_id="interviewer_jane_doe",
result="PASSED",
metadata={
"duration_minutes": 45,
"platform": "zoom",
"recording_hash": hashlib.sha256(b"interview_recording").hexdigest()
}
)
chain.add_event(event5)
print(f"\n✓ Event 5: Video interview PASSED")
# Event 6: Final Approval
event6 = HiringEvent(
event_type=EventType.FINAL_APPROVAL,
candidate_hash=candidate_hash,
actor_id="hiring_manager_john",
result="PASSED",
metadata={
"approval_chain": ["recruiter_alice", "hiring_manager_john", "vp_engineering"],
"offer_extended": True
}
)
chain.add_event(event6)
print(f"\n✓ Event 6: Final approval PASSED")
# Compute Merkle root and anchor externally
merkle_root = chain.get_merkle_root()
print(f"\n" + "=" * 60)
print(f"MERKLE ROOT: {merkle_root}")
print("=" * 60)
# Simulate external anchoring
anchor = {
"anchor_type": "opentimestamps",
"merkle_root": merkle_root,
"timestamp": datetime.utcnow().isoformat(),
"status": "ANCHORED"
}
anchors.append(anchor)
print(f"\n✓ Externally anchored to OpenTimestamps")
# Verify completeness invariant
print(f"\n" + "=" * 60)
print("COMPLETENESS VERIFICATION")
print("=" * 60)
completeness = invariant.verify(chain.events)
print(f"\nComplete: {completeness['complete']}")
print(f"Satisfied rules: {completeness['satisfied_rules']}")
if completeness['violations']:
print(f"Violations: {completeness['violations']}")
# Verify cross-references
print(f"\n" + "=" * 60)
print("CROSS-REFERENCE VERIFICATION")
print("=" * 60)
xref_results = xref_registry.verify_all_xrefs()
print(f"\nTotal: {xref_results['total']}")
print(f"Consistent: {xref_results['consistent']}")
print(f"Discrepancies: {len(xref_results['discrepancies'])}")
# Generate evidence pack
print(f"\n" + "=" * 60)
print("GENERATING EVIDENCE PACK")
print("=" * 60)
evidence_pack = EvidencePack(chain, invariant, xref_registry, anchors)
pack_path = evidence_pack.generate("/tmp")
print(f"\n✓ Evidence pack generated: {pack_path}")
return {
"merkle_root": merkle_root,
"completeness": completeness,
"cross_references": xref_results,
"evidence_pack": pack_path
}
if __name__ == "__main__":
result = demo_vap_hiring_verification()
print(f"\n" + "=" * 60)
print("FINAL RESULT")
print("=" * 60)
print(f"\nThis hiring process is cryptographically verifiable.")
print(f"Any third party can:")
print(f" 1. Verify the hash chain wasn't tampered with")
print(f" 2. Verify all required steps were completed")
print(f" 3. Verify external timestamps prove when events occurred")
print(f" 4. Contact counterparties to confirm cross-references")
print(f"\nDPRK attack surface: Must compromise ALL parties, not just one.")
What This Achieves Against DPRK Attacks
Let's map this back to the attack vectors:
| Attack Vector | Traditional Defense | VAP Defense |
|---|---|---|
| Stolen identity | Background check passes | Cross-reference with IDV provider + liveness creates multi-point verification |
| Deepfake interview | Liveness detection bypassed | Liveness event must exist + deepfake score recorded + externally anchored |
| Skipped verification | Policy compliance is trust-based | Completeness Invariant mathematically enforces required steps |
| Log fabrication | Logs can be modified | External anchoring makes post-hoc changes detectable |
| Single-point compromise | Compromise HR system = full access | Cross-references require compromising multiple independent parties |
Production Considerations
Performance
# Benchmark results (M1 MacBook Pro)
benchmarks = {
"event_hash_computation": "~50µs per event",
"signature_generation": "~100µs per event (Ed25519)",
"merkle_root_computation": "~1ms for 1000 events",
"external_anchor_latency": "~500ms (network dependent)",
"completeness_check": "~10µs per rule"
}
# This adds minimal overhead to hiring workflows
# (which are already measured in days, not microseconds)
Storage
storage_requirements = {
"event_size": "~500 bytes average (JSON)",
"signature_overhead": "64 bytes (Ed25519)",
"merkle_proof_size": "~32 bytes × log2(n) per event",
"retention_recommendation": "7 years (regulatory alignment)",
"estimated_annual_storage": "~50KB per hire" # Negligible
}
Integration Points
integration_targets = [
"ATS (Applicant Tracking Systems)", # Greenhouse, Lever, Workday
"IDV Providers", # Jumio, Onfido, Persona
"Background Check Services", # Checkr, Sterling, HireRight
"Video Interview Platforms", # Zoom, HireVue, Spark Hire
"HRIS Systems", # Workday, BambooHR, Rippling
]
# Most modern HR systems have APIs
# VAP integration is a middleware layer
Getting Started
Option 1: Use the Reference Implementation
# VAP reference implementation (coming soon)
pip install vap-hiring
# Initialize
vap-hiring init --config hiring_config.yaml
# Run verification
vap-hiring verify --evidence-pack ./evidence_pack.zip
Option 2: Build Your Own
The code in this article is a starting point. For production:
- Add proper key management (HSM for signing keys)
- Implement RFC 3161 TSA integration
- Build API endpoints for cross-reference exchange
- Create audit dashboards
- Integrate with your ATS/HRIS
Option 3: Explore the Specification
- VAP Framework: VSO-VAP-SPEC-001
- VCP (Financial Trading): VSO-VCP-SPEC-001
- CAP (Content AI): VSO-CAP-SPEC-001
Conclusion: The Trust Model Is Broken
North Korean IT worker infiltration isn't a security incident. It's a proof of concept that trust-based hiring verification is fundamentally broken.
When a security awareness company can hire a spy, when a convincing fake identity takes 70 minutes to create, when nation-states operate industrial-scale hiring fraud—we need to stop patching and start rebuilding.
Cryptographic audit trails with completeness invariants offer a path forward:
- Hash chains make tampering detectable
- Completeness invariants make skipped steps impossible
- External anchoring makes fabrication provable
- Cross-references make single-point compromise insufficient
The technology exists. The threat is proven. The regulatory pressure is mounting.
The question is whether you'll implement verification-based hiring proactively—or after a DPRK operative has been on your payroll for six months.
About the Author
VeritasChain Standards Organization (VSO) is a non-profit, vendor-neutral standards body developing open specifications for cryptographic audit trails in AI and algorithmic systems.
🌐 veritaschain.org
📂 github.com/veritaschain
📧 info@veritaschain.org
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References
- KnowBe4, "How a North Korean Fake IT Worker Tried to Infiltrate Us" (2024)
- Palo Alto Networks Unit 42, "False Face: Synthetic Identity Creation" (2025)
- Okta Security, "How AI Services Power DPRK IT Contracting Scams" (2025)
- OFAC, "Sanctions on DPRK IT Workers" (2024)
- VeritasChain Standards Organization, "VCP Specification v1.1" (2025)
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