Summary

Based on the data you provided, this is a log file of security activity analysis. It appears to be from SecureDep, an AI-driven security platform that monitors software vulnerabilities (CVEs), identifies threats, and monitors agents for vulnerabilities in its codebase.

Here is a breakdown of the key data points and how to interpret the findings, followed by a Python script to visualize the data.

### 1. Key Observations

Here are the critical metrics extracted from your log:

* Overall Security Status:
* Safe Deploys: 1 (only `tokio`)
* Malicious: 27
* Total Activity: 1,049 entries
* High Severity: 95% (27 out of 28 entries flagged as high-risk)
* Threat Types Identified (High Risk):
* API Agents: Many entries from "postman..." (Postman, Postmanwdio, etc.), indicating automated agents injecting code into APIs.
* Web Scrapers: `web-scraper-mcp` detected (likely scraping for data).
* Suspicious Code: `postmanwdio-a`, `postmannode-k`, etc. (Generic or specific agent signatures).
* Sandboxing/Containers: `vf-oss-template`, `zuper-sdk`, `wenk`, `fastapi`, `express`, `react-dom`.
* Timestamps:
* Nov 24, 2025 (Current or Near-Future).
* Jan 23–24, 2025 (Recent Activity).
* Jan 6, 2026 (Future Scheduled Activity).
* Tools Used:
* Backend: FastAPI, Express, Postman, MCP Servers.
* Frontend: React, Vue.
* Agent SDK: Wenk, Victoria, Zuper (Sandboxing tools).
* Infrastructure: Vault (Sandbox), Vault-EVM.

---

### 2. Visualization Guide
You can visualize this data using simple charting tools or code.

#### Option A: Simple Line Chart (Python/Google Chart)
You can create a visual map of threat types and severity based on the log.

```python
import matplotlib.pyplot as plt

# Data from your log snippet
threat_types = [
"API Agents",
"Web Scrapers",
"Sandboxing Tools"
]
severity_levels = [
"High",
"Safe"
]

severity_counts = [
"High",
"High",
"High"
]

# Create the figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Plotting: Y-axis is Severity, X-axis is Threat Type
# Assuming 'X' and 'Y' are column names or indices
# Note: In a real dashboard, these might be named 'threats' or 'types'.
for i, (type, sev) in enumerate(zip(threat_types, severity_levels), 1):
ax.plot([1, 2, 3], [sev_counts[i]], color='red', linestyle='dotted', label=f'High Risk ({sev})')
ax.plot([1, 2, 3], [sev_counts[i]], color='orange', linestyle='dotted', label=f'Safe ({sev})')

# Add markers for the Safe one
ax.scatter([0], [severity_levels[0]], color='blue', s=100, label='Safe', marker='o')
ax.plot([1, 2, 3], [severity_levels[1]], color='green', linestyle='solid', label='Safe')

# Set labels and title
ax.set_xlabel('Threat Type')
ax.set_ylabel('Severity Level')
ax.set_title('Security Analysis Log: 2025 Nov')
ax.legend()
ax.grid(True, alpha=0.3)

# Show legend
plt.legend()

plt.tight_layout()
plt.savefig("security_analysis_v2.png", dpi=300)
print("Figure saved as 'security_analysis_v2.png'")
```

#### Option B: Interactive Data Table (Excel/Power BI style)
Create a table with columns: `Date`, `Tool`, `Agent`, `Threat Type`, `Severities`.

| Date | Tool | Agent | Threat Type | Severity | Notes |
| :--- | :--- | :--- | :--- | :--- | :--- |
| Mon 24/11 | Postmanwdio-a | 0.0.7 | API Agent | High | Automated injection detected. |
| Tue 25/11 | Victoria-wallet | 0.1.2 | Sandbox | High | Sandboxing detected by vault. |
| Thu 23/11 | Requests | 2.28.1 | Standard | High | External SDK usage. |

---

### 3. Analysis of the "Malicious" Pattern
Based on the data, the following patterns are suspicious:

1. Postman/Postmanwdio Injection:
* The log shows entries for `postmanwdio-a` (Postmanwdio) and `postmanwdio-j`. These are known automated APIs often used to fetch data or inject scripts.
* *Risk:* These agents are likely trying to scrape data from external sources or automate the application's request handling.
2. Suspicious SDK Names:
* You see `vf-oss-template`, `zuper-sdk`, `wenk`. These look like sandboxing environments.
* *Risk:* Agents running inside containers or VMs might be executing sensitive data or bypassing sandbox checks.
3. Timing:
* The most recent activity (`Nov 24, 2025`) was 4:00 PM.
* The next scheduled activity (`Jan 6, 2026`) is 7:00 PM, suggesting the monitoring tool is likely configured for a specific 24-hour window (e.g., "Monitor agents during this hour").

### Conclusion
The logs indicate an active AI-Agent Monitoring campaign where agents are actively scanning for vulnerabilities (`tokio` is Safe), identifying threats (`API Agents`), and managing sandboxed environments (`Victoria`, `Zuper`). The primary risk appears to be automated injection via Postman agents and sandboxing via Vault.

Title

SafeDep — Real-time Open Source Software Supply Chain Security

Description

SafeDep continuously scans packages published in npm, PyPI, RubyGems, and more for malicious code, protecting software development teams at different stages of the software supply chain.

Keywords

view, high, report, safe, wallet, agent, victoria, packages, start, block, quick, time, threats, threat, template, agents, real

NS Lookup

A 104.21.66.249, A 172.67.166.138

Dates

Created 2026-03-09

Updated 2026-04-15

Summarized 2026-04-24