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security: SSRF hardening, log injection sanitization, and allow_local_smtp

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Add SSRF host validation to maintenance.py send_nightly_email and
scheduler.py send_zombie_alert_email using validate_external_url().
New allow_local_smtp config key permits private-IP SMTP for local
relay setups.

Add sanitize_name() to security_utils and apply it to all log calls
in message_handler, repeater_manager, path_command, solarforecast_command,
command_manager, and discord_bridge_service to prevent log injection.

Move nightly email logic from duplicate scheduler._send_nightly_email()
into the canonical maintenance.py implementation, removing the duplicate.
Update tests to call maintenance.send_nightly_email() directly.

Add validate_external_url allow_private parameter with support for
loopback, RFC1918, CGN, and link-local address ranges.
This commit is contained in:
Stacy Olivas
2026-04-14 10:02:36 -07:00
committed by agessaman
parent c7fa0ba3d2
commit 54aeb28bf0
18 changed files with 707 additions and 80 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/commands/path_command.py
+17 -16
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@@ -11,6 +11,7 @@ import time
from typing import Any, Callable, Optional
from ..models import MeshMessage
from ..security_utils import sanitize_name
from ..utils import calculate_distance, parse_path_string
from .base_command import BaseCommand
@@ -769,7 +770,7 @@ class PathCommand(BaseCommand):
# Apply star bias multiplier if repeater is starred
if repeater.get('is_starred', False):
combined_score *= self.star_bias_multiplier
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {repeater.get('name', 'unknown')}")
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {sanitize_name(repeater.get('name', 'unknown'))}")
# SNR bonus: If repeater has SNR data, it's a zero-hop repeater (direct neighbor)
# This is strong evidence it's close and should be preferred
@@ -778,7 +779,7 @@ class PathCommand(BaseCommand):
# Add bonus proportional to zero-hop bonus (20% of combined score)
snr_bonus = combined_score * 0.2
combined_score += snr_bonus
self.logger.debug(f"SNR bonus for {repeater.get('name', 'unknown')}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
self.logger.debug(f"SNR bonus for {sanitize_name(repeater.get('name', 'unknown'))}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
combined_scores.append((combined_score, distance, repeater))
@@ -1165,7 +1166,7 @@ class PathCommand(BaseCommand):
# Apply star bias multiplier if repeater is starred
if repeater.get('is_starred', False):
combined_score *= self.star_bias_multiplier
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {repeater.get('name', 'unknown')}")
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {sanitize_name(repeater.get('name', 'unknown'))}")
# SNR bonus: If repeater has SNR data, it's a zero-hop repeater (direct neighbor)
# This is strong evidence it's close and should be preferred
@@ -1174,7 +1175,7 @@ class PathCommand(BaseCommand):
# Add bonus proportional to zero-hop bonus (20% of combined score)
snr_bonus = combined_score * 0.2
combined_score += snr_bonus
self.logger.debug(f"SNR bonus for {repeater.get('name', 'unknown')}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
self.logger.debug(f"SNR bonus for {sanitize_name(repeater.get('name', 'unknown'))}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
if combined_score > best_combined_score:
best_combined_score = combined_score
@@ -1248,7 +1249,7 @@ class PathCommand(BaseCommand):
# Apply star bias multiplier if repeater is starred
if repeater.get('is_starred', False):
combined_score *= self.star_bias_multiplier
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {repeater.get('name', 'unknown')}")
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {sanitize_name(repeater.get('name', 'unknown'))}")
# SNR bonus: If repeater has SNR data, it's a zero-hop repeater (direct neighbor)
# This is strong evidence it's close and should be preferred
@@ -1257,7 +1258,7 @@ class PathCommand(BaseCommand):
# Add bonus proportional to zero-hop bonus (20% of combined score)
snr_bonus = combined_score * 0.2
combined_score += snr_bonus
self.logger.debug(f"SNR bonus for {repeater.get('name', 'unknown')}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
self.logger.debug(f"SNR bonus for {sanitize_name(repeater.get('name', 'unknown'))}: +{snr_bonus:.3f} (has SNR data, confirmed zero-hop)")
all_scores.append((repeater.get('name', 'unknown'), distance, recency_score, proximity_score, combined_score))
@@ -1358,7 +1359,7 @@ class PathCommand(BaseCommand):
stored_to_key = prev_to_candidate_edge.get('to_public_key', '').lower() if prev_to_candidate_edge.get('to_public_key') else None
if stored_to_key and stored_to_key == candidate_public_key:
stored_key_bonus = max(stored_key_bonus, 0.4) # Strong bonus for matching stored key
self.logger.debug(f"Found stored public key match for {repeater.get('name', 'unknown')} in edge {prev_norm}->{candidate_norm}")
self.logger.debug(f"Found stored public key match for {sanitize_name(repeater.get('name', 'unknown'))} in edge {prev_norm}->{candidate_norm}")
# Check edge from candidate to next node
if next_norm:
@@ -1367,7 +1368,7 @@ class PathCommand(BaseCommand):
stored_from_key = candidate_to_next_edge.get('from_public_key', '').lower() if candidate_to_next_edge.get('from_public_key') else None
if stored_from_key and stored_from_key == candidate_public_key:
stored_key_bonus = max(stored_key_bonus, 0.4) # Strong bonus for matching stored key
self.logger.debug(f"Found stored public key match for {repeater.get('name', 'unknown')} in edge {candidate_norm}->{next_norm}")
self.logger.debug(f"Found stored public key match for {sanitize_name(repeater.get('name', 'unknown'))} in edge {candidate_norm}->{next_norm}")
# Zero-hop bonus: If this repeater has been heard directly by the bot (zero-hop advert),
# it's strong evidence it's close and should be preferred, even for intermediate hops.
@@ -1378,7 +1379,7 @@ class PathCommand(BaseCommand):
if hop_count is not None and hop_count == 0 and graph_score > 0:
# This repeater has been heard directly - strong evidence it's close to bot
zero_hop_bonus = self.graph_zero_hop_bonus
self.logger.debug(f"Zero-hop bonus for {repeater.get('name', 'unknown')}: {zero_hop_bonus:.2%} (heard directly by bot)")
self.logger.debug(f"Zero-hop bonus for {sanitize_name(repeater.get('name', 'unknown'))}: {zero_hop_bonus:.2%} (heard directly by bot)")
# SNR bonus: If this repeater has SNR data, it's a zero-hop repeater (direct neighbor)
# This is even stronger evidence than just hop_count == 0, as it means we have actual signal quality data.
@@ -1389,7 +1390,7 @@ class PathCommand(BaseCommand):
# SNR presence indicates zero-hop connection with signal quality data
# Use same bonus as zero-hop, but this is more definitive
snr_bonus = self.graph_zero_hop_bonus * 1.2 # 20% stronger than zero-hop bonus alone
self.logger.debug(f"SNR bonus for {repeater.get('name', 'unknown')}: {snr_bonus:.2%} (has SNR data, confirmed zero-hop)")
self.logger.debug(f"SNR bonus for {sanitize_name(repeater.get('name', 'unknown'))}: {snr_bonus:.2%} (has SNR data, confirmed zero-hop)")
# Add stored key bonus, zero-hop bonus, and SNR bonus to graph score
graph_score_with_bonus = min(1.0, graph_score + stored_key_bonus + zero_hop_bonus + snr_bonus)
@@ -1454,7 +1455,7 @@ class PathCommand(BaseCommand):
path_validation_bonus = max(path_validation_bonus, segment_bonus + obs_bonus)
# Cap at max bonus
path_validation_bonus = min(self.graph_path_validation_max_bonus, path_validation_bonus)
self.logger.debug(f"Path validation match for {repeater.get('name', 'unknown')}: {common_segments} common segments (obs: {obs_count})")
self.logger.debug(f"Path validation match for {sanitize_name(repeater.get('name', 'unknown'))}: {common_segments} common segments (obs: {obs_count})")
if path_validation_bonus >= self.graph_path_validation_max_bonus * 0.9:
break # Strong match found
except Exception as e:
@@ -1521,7 +1522,7 @@ class PathCommand(BaseCommand):
# Apply penalty: up to penalty_strength reduction
penalty = normalized_excess * self.graph_distance_penalty_strength
candidate_score = candidate_score * (1.0 - penalty)
self.logger.debug(f"Applied distance penalty to {repeater.get('name', 'unknown')}: {max_distance:.1f}km hop (penalty: {penalty:.2%}, score: {candidate_score:.3f})")
self.logger.debug(f"Applied distance penalty to {sanitize_name(repeater.get('name', 'unknown'))}: {max_distance:.1f}km hop (penalty: {penalty:.2%}, score: {candidate_score:.3f})")
elif max_distance > 0:
# Even if under threshold, very long hops should get a small penalty
# This helps prefer shorter hops when graph evidence is similar
@@ -1549,7 +1550,7 @@ class PathCommand(BaseCommand):
# Apply max distance threshold if configured
if self.graph_final_hop_max_distance > 0 and distance > self.graph_final_hop_max_distance:
# Beyond max distance - skip proximity bonus
self.logger.debug(f"Final hop candidate {repeater.get('name', 'unknown')} is {distance:.1f}km from bot, beyond max distance {self.graph_final_hop_max_distance:.1f}km")
self.logger.debug(f"Final hop candidate {sanitize_name(repeater.get('name', 'unknown'))} is {distance:.1f}km from bot, beyond max distance {self.graph_final_hop_max_distance:.1f}km")
else:
# Normalize distance to 0-1 score (inverse: closer = higher score)
# Use configurable normalization distance (default 500km for more aggressive scoring)
@@ -1569,14 +1570,14 @@ class PathCommand(BaseCommand):
# Combine with graph score using effective weight
candidate_score = candidate_score * (1.0 - effective_weight) + proximity_score * effective_weight
self.logger.debug(f"Final hop proximity for {repeater.get('name', 'unknown')}: distance={distance:.1f}km, proximity_score={proximity_score:.3f}, effective_weight={effective_weight:.3f}, combined_score={candidate_score:.3f}")
self.logger.debug(f"Final hop proximity for {sanitize_name(repeater.get('name', 'unknown'))}: distance={distance:.1f}km, proximity_score={proximity_score:.3f}, effective_weight={effective_weight:.3f}, combined_score={candidate_score:.3f}")
else:
# Repeater without valid location data - apply significant penalty for final hop
# This ensures we prefer repeaters with known locations, especially direct neighbors
# Penalty: reduce score by 50% (repeaters with location data will have proximity bonus, so this creates strong preference)
location_penalty = 0.5
candidate_score = candidate_score * (1.0 - location_penalty)
self.logger.debug(f"Final hop candidate {repeater.get('name', 'unknown')} has no valid location data - applying {location_penalty:.0%} penalty (score: {candidate_score:.3f})")
self.logger.debug(f"Final hop candidate {sanitize_name(repeater.get('name', 'unknown'))} has no valid location data - applying {location_penalty:.0%} penalty (score: {candidate_score:.3f})")
# Apply star bias multiplier if repeater is starred
# Starred repeaters should get significant advantage in graph selection
@@ -1586,7 +1587,7 @@ class PathCommand(BaseCommand):
candidate_score *= self.star_bias_multiplier
# Cap at 1.0 but allow it to exceed temporarily for comparison
# We'll normalize later when converting to confidence
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {repeater.get('name', 'unknown')} in graph selection (score: {candidate_score:.3f})")
self.logger.debug(f"Applied star bias ({self.star_bias_multiplier}x) to {sanitize_name(repeater.get('name', 'unknown'))} in graph selection (score: {candidate_score:.3f})")
if candidate_score > best_score:
best_score = candidate_score