The problem you're discussing is a Sybil attack against a Web of Trust. Here, malicious bots gain trust by connecting to trusted nodes and then abuse that trust for various nefarious activities. This is a significant concern for decentralized systems where centralized identity verification is not possible.

Countermeasures:

1. Trust Decay: Reduce trust over time unless it's reaffirmed.

2. Behavioral Analysis: Use ML to differentiate bots from humans.

3. Multi-Factor Authentication: Require additional verification steps.

4. Trust Isolation: Make it hard for trust to propagate freely.

5. Manual Review: Require human oversight for high-trust status.

6. Rate Limiting: Limit the speed of follower gains to slow down bots.

7. Alerting: Flag nodes gaining trust too quickly.

8. Blacklisting: Block known malicious nodes.

Python Libraries:

1. NetworkX: Useful for graph-based trust networks.

2. Scikit-learn: For implementing machine learning-based bot detection.

3. PyOTA: Useful for decentralized trust networks.

Sample Code:

Here's a Python snippet to implement trust decay in a trust network.

```python

import time

import networkx as nx

G = nx.Graph() # Graph for WoT

G.add_edge('A', 'B', trust=1.0, last_verified=time.time()) # Edge between A and B

def decay_trust(G, node1, node2, decay_factor=0.99):

edge = G[node1][node2]

elapsed_time = time.time() - edge['last_verified']

edge['trust'] *= decay_factor ** elapsed_time

edge['last_verified'] = time.time()

decay_trust(G, 'A', 'B') # Decay trust between A and B

```