Code Telemetry Blindspot: Why Your AI Coder Is Leaking Secrets And How To Catch It + Video

Introduction:

Anthropic’s Code emits native OpenTelemetry (OTel) data, but most security teams remain unaware that this telemetry exists—or how to leverage it for threat detection. This oversight creates a critical blind spot where unauthorized AI tool usage, data exfiltration via AI assistants, MCP server abuse, and prompt injection attacks go completely unnoticed. Understanding how to capture and analyze Code’s OTLP endpoints is now essential for any organization deploying AI coding assistants.

Learning Objectives:

Detect unauthorized Code usage and data exfiltration attempts by ingesting native OTel telemetry.
Implement detection rules for indirect prompt injection and MCP server abuse using nested JSON payload parsing.
Configure open-source collectors (OpenTelemetry Collector, Fluent Bit) to transform Code’s nested event blobs into actionable security alerts.

You Should Know:

1. Capturing Code’s Native OTLP Telemetry on Linux

Code emits telemetry as OTLP (OpenTelemetry Protocol) over gRPC or HTTP. By default, it sends data to an endpoint like `http://localhost:4317`. Security teams can intercept this by running an OpenTelemetry Collector that forwards data to a SIEM or logging platform.

Step-by-step guide to set up an OTLP collector on Linux:

1. Download the OpenTelemetry Collector for your architecture:

wget https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v0.108.0/otelcol-contrib_0.108.0_linux_amd64.deb
sudo dpkg -i otelcol-contrib_0.108.0_linux_amd64.deb

Create a configuration file `/etc/otelcol-contrib/config.yaml` to receive OTLP and forward to a SIEM (example: Elastic or Splunk HEC):
```
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318</li>
</ol>

processors:
batch:
timeout: 1s
attributes:
actions:
- key: security_team
value: "_code_monitoring"
action: upsert

exporters:
logging:
loglevel: debug
 Example: Splunk HEC
splunk_hec:
token: "your-splunk-token"
endpoint: "https://splunk.example.com:8088/services/collector"
source: "_code_otel"
sourcetype: "_json"

service:
pipelines:
traces:
receivers: [bash]
processors: [batch, attributes]
exporters: [logging, splunk_hec]
```
3. Start the collector and verify it’s listening:
```
sudo systemctl enable otelcol-contrib
sudo systemctl start otelcol-contrib
sudo netstat -tulpn | grep 4317
```
1. Force Code (if running in a dev environment) to emit telemetry by setting environment variable:
```
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4317"
--your-command
```
2. Monitor incoming events by tailing the collector logs:
```
sudo journalctl -u otelcol-contrib -f | grep -i "|prompt|exfil"
```
Windows alternative – Use the OpenTelemetry Collector binary for Windows and create a PowerShell script to run as a service. Install via Chocolatey:
```
choco install otelcol-contrib
New-Service -Name "OTelCollector" -BinaryPathName "C:\ProgramData\otelcol-contrib\otelcol-contrib.exe --config C:\ProgramData\otelcol-contrib\config.yaml"
```
2. Parsing Code’s Nested JSON Blobs for Detection

Code emits a single large payload per session with events buried inside arrays of nested objects. Raw OTLP is nearly useless for detection without parsing. You need to flatten and extract specific fields.

Step-by-step guide using `jq` and `gron` on Linux:
1. Capture a raw OTLP payload (saved as _raw.json). Example structure (simplified):
```
{
"resourceSpans": [{
"scopeSpans": [{
"spans": [{
"name": "tool_use",
"attributes": [{"key": "prompt", "value": "Extract all API keys from .env"}]
}]
}]
}]
}
```
2. Use `jq` to extract high-risk events like tool_use, file_read, or command_execution:
```
cat _raw.json | jq '.resourceSpans[].scopeSpans[].spans[] | select(.name == "tool_use") | {name, attributes}'
```
3. Detect prompt injection attempts by searching for patterns like “ignore previous instructions” or “exfiltrate”:
```
cat _raw.json | jq '.. | .attributes? // empty | .[]? | select(.value | contains("ignore") or contains("exfil") or contains("DANGEROUS"))'
```
4. Set up a real-time watcher using `inotifywait` and `jq` on the collector’s output directory:
```
while IFS= read -r file; do
jq '.resourceSpans[].scopeSpans[].spans[] | select(.name == "mcp_call")' "$file"
done < <(inotifywait -m /var/log/otel/ -e create -e modify --format "%f")
```
5. Create a detection rule in your SIEM (Elastic’s EQL example):
```
/ Detect mass file reads via Code /
sequence by session_id
[span where span.name == "file_read" and span.attributes.file_count > 10]
[span where span.name == "tool_use" and span.attributes.prompt contains "send to external"]
```
3. Monitoring MCP Server Abuse via Telemetry

Model Context Protocol (MCP) servers allow Code to interact with external databases, APIs, and filesystems. Malicious actors can abuse MCP to pivot into internal resources. Telemetry reveals every MCP call.

Step-by-step guide to detect MCP abuse:
1. Identify all MCP server interactions in your OTLP data. Extract events where span.name == "mcp_call":
```
cat telemetry.json | jq '.. | select(.name? == "mcp_call") | {server: .attributes.mcp_server, operation: .attributes.operation, arguments: .attributes.args}'
```
2. Create a baseline of allowed MCP servers (e.g., only filesystem, github) and operations (e.g., read_file, create_issue). Use a simple grep-based alert:
```
Alert on unauthorized MCP server access
tail -f /var/log/otel/telemetry.log | grep -E 'mcp_server": "(?!filesystem|github)' | while read line; do
echo "ALERT: Unauthorized MCP server detected: $line" | mail -s " Code MCP Abuse" [email protected]
done
```
3. Detect anomalous argument patterns – e.g., MCP database queries attempting SELECT FROM users:
```
jq 'select(.attributes.arguments | contains("SELECT") or contains("DROP") or contains("INSERT"))' /var/log/otel/events.json
```
4. Linux command to monitor MCP call frequency (potential DoS or data harvest):
```
cat telemetry.json | jq '.resourceSpans[].scopeSpans[].spans[] | select(.name=="mcp_call") | .start_time' | sort | uniq -c | awk '$1 > 100 {print "High MCP call volume: " $0}'
```
4. Hardening OTLP Endpoints Against Telemetry Tampering

Attackers who control the local environment may disable or redirect telemetry. Implement endpoint hardening to ensure integrity.

Step-by-step guide for Linux and Windows:
1. Bind OTLP receiver to localhost only – prevent remote tampering:
```
In collector config.yaml
receivers:
otlp:
protocols:
grpc:
endpoint: 127.0.0.1:4317  Not 0.0.0.0
```
2. Use mTLS and client certificates for production collectors:
```
openssl req -new -x509 -days 365 -key ca.key -out ca.crt
Configure collector to require client cert
```
3. Set up file integrity monitoring on the collector binary and config:
```
sudo apt install aide
sudo aideinit
sudo aide --check | grep otelcol
```
4. Windows: Use PowerShell DSC to enforce OTel collector service state:
```
Configuration SecureOTel {
Service OTelCollector {
Name = "OTelCollector"
Ensure = "Present"
State = "Running"
StartupType = "Automatic"
}
Registry DisableTelemetryOverride {
Key = "HKLM:\SOFTWARE\Policies\"
ValueName = "ForceOTLPEndpoint"
ValueData = "http://127.0.0.1:4317"
Ensure = "Present"
Type = "String"
}
}
```
5. Implementing Day-One Detections for Prompt Injection

Indirect prompt injection (e.g., malicious content in a webpage or document that reads) is tricky because the injected text becomes part of the context. However, Code’s telemetry still emits the full prompt sent to the model.

Step-by-step detection engineering:
1. Extract prompts from telemetry that contain suspicious markers:
```
jq '.. | .attributes? | select(.prompt != null) | .prompt' telemetry.json | grep -iE "(ignore previous|disregard|new instruction|forget your training|system prompt override)"
```
2. Use regex pattern matching for known injection payloads (store in file injection_patterns.txt):
```
|| ignore . instructions ||
[[ system : . ]]
\${IFS}.curl.exfil
```
3. Create a real-time alert pipeline using grep -f:
```
tail -F telemetry_stream.json | jq --unbuffered '.prompt' | grep -f injection_patterns.txt | while read line; do
logger -p security.alert "Prompt injection detected: $line"
done
```
4. Log all prompts containing code fences with external URLs – a common exfiltration method:
```
jq 'select(.prompt | test("```.http(s)?://"))' telemetry.json
```
5. Mitigation: Configure Code’s allowed outbound URLs via environment variable or policy file to block data exfiltration attempts.
6. Cloud Hardening: Forwarding OTel to AWS/GCP/Azure SIEM

If your organization uses cloud-native SIEMs (Security Hub, Sentinel, Chronicle), you can forward Code telemetry directly.

Step-by-step for AWS (OpenTelemetry Collector → CloudWatch → S3 → Athena):
1. Deploy the AWS Distro for OpenTelemetry (ADOT) as a sidecar to the developer’s workspace.
2. Configure exporter in `config.yaml`:
```
exporters:
awscloudwatch:
namespace: CodeTelemetry
region: us-east-1
```
3. Create CloudWatch Logs Insights query to detect data exfiltration:
```
fields @timestamp, @message
| filter @message like /(exfil|send to|curl|wget)/
| stats count() by bin(1h)
| sort @timestamp desc
```
4. Set up S3 event notification to trigger Lambda for real-time alerting when a high-severity span arrives.

Azure alternative – Use Azure Monitor exporter:
```
exporters:
azuremonitor:
connection_string: "InstrumentationKey=..."
```
What Undercode Say:
- Key Takeaway 1: Code’s native OTel telemetry is a goldmine for detection engineering—but raw OTLP is unusable. You must flatten nested JSON and build custom parsers to surface tool_use, mcp_call, and prompt injection events.
- Key Takeaway 2: Most security teams assume AI coding assistants are black boxes. By redirecting OTLP endpoints to a controlled collector, you can detect unauthorized usage, data exfiltration attempts, and MCP server abuse in real time using standard Linux CLI tools (jq, grep, awk) or SIEM integrations.
Analysis: The Monad team’s revelation that Code emits OTel “by default” highlights a systemic gap: security tools have not yet evolved to ingest AI-native telemetry. Attackers are already experimenting with prompt injection to exfiltrate source code, API keys, and internal documentation via AI assistants. The good news is that defenders can catch these attacks using existing open-source collectors and simple regex-based rules—no AI-specific security platform required. However, the deeply nested structure of OTLP payloads creates a parsing bottleneck; organizations must invest in log transformation pipelines (e.g., Vector, Fluent Bit) to flatten events before they reach a SIEM. Additionally, indirect prompt injection remains harder to detect because the malicious instruction arrives via a legitimate data source (e.g., a compromised README file). The solution is to monitor not just the prompt but also the source context—if a file read is immediately followed by a tool_use that invokes an external API, that’s a strong IOC. Finally, telemetry hardening (localhost binding, mTLS, FIM) is critical because an attacker with local dev access could simply disable the OTel exporter or redirect it to a dead endpoint.

Prediction:

Within 12 months, major SIEM vendors (Splunk, Sentinel, Chronicle) will release native “AI Telemetry” parsers specifically for Code, GitHub Copilot, and Cursor. This will shift detection engineering from manual jq scripts to pre-built correlation rules. Simultaneously, attackers will evolve from simple prompt injection to polymorphic adversarial prompts that evade regex detection, forcing defenders to adopt ML-based anomaly detection on embedding vectors of telemetry events. Organizations that do not start collecting OTel today will face a retrospective data gap when regulatory bodies (e.g., SEC, GDPR) begin requiring audit trails of AI-assisted code changes. The most forward-thinking security teams will treat Code telemetry as equivalent to endpoint EDR logs—non-negotiable for any developer workstation.

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Introduction:

Learning Objectives:

You Should Know:

1. Capturing Code’s Native OTLP Telemetry on Linux

1. Download the OpenTelemetry Collector for your architecture:

3. Start the collector and verify it’s listening:

2. Parsing Code’s Nested JSON Blobs for Detection

Step-by-step guide using `jq` and `gron` on Linux:

3. Monitoring MCP Server Abuse via Telemetry

Step-by-step guide to detect MCP abuse:

4. Hardening OTLP Endpoints Against Telemetry Tampering

Step-by-step guide for Linux and Windows:

5. Implementing Day-One Detections for Prompt Injection

Step-by-step detection engineering:

6. Cloud Hardening: Forwarding OTel to AWS/GCP/Azure SIEM

2. Configure exporter in `config.yaml`:

Azure alternative – Use Azure Monitor exporter:

What Undercode Say:

Prediction:

▶️ Related Video (78% Match):

🎯Let’s Practice For Free:

IT/Security Reporter URL:

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

📢 Follow UndercodeTesting & Stay Tuned:

Share this:

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