Alright folks, I've been wrestling with the allowlist problem for our LangChain and custom agent deployments. The default stance of most runtimes is to request overly permissive egress, often blanket HTTPS out, which is a non-starter for a proper security posture. Manually curating these lists is tedious and error-prone, especially when dependencies or runtime versions change.
I finally carved out some time to build a pragmatic tool to help. It's a Python script that parses structured logs (or network captures) from a controlled test run of your agent workflow and outputs a candidate allowlist, typically in iptables or nftables format. The core idea is simple: run your agents in a monitored, isolated sandbox during a comprehensive integration test, record all unique outbound connections, and generate the minimal rules needed for that specific workload.
Here's the basic flow and a snippet of the parser core:
```python
import json
import ipaddress
from collections import defaultdict
def parse_connection_log(log_file_path):
"""Parses a JSONL log where each line has 'dest_ip' and 'dest_port'."""
destinations = defaultdict(set)
with open(log_file_path, 'r') as f:
for line in f:
try:
entry = json.loads(line.strip())
ip = entry.get('dest_ip')
port = entry.get('dest_port')
if ip and port:
# Basic validation and normalization
ip_obj = ipaddress.ip_address(ip)
destinations[str(ip_obj)].add(int(port))
except (json.JSONDecodeError, ValueError) as e:
continue # or log the error
return destinations
def generate_iptables_rules(destinations, protocol="tcp"):
"""Generates iptables ACCEPT rules for each unique IP:Port combo."""
rules = []
for ip, ports in destinations.items():
for port in sorted(ports):
# You'd likely want to specify the network interface and chain
rule = f"-A OUTPUT -d {ip}/32 -p {protocol} --dport {port} -j ACCEPT"
rules.append(rule)
return rules
```
**Key considerations I baked in:**
* **Granularity:** It outputs per-IP and per-port rules. This is stricter than allowing whole CIDR ranges, but you can modify the script to aggregate if you trust the entire netblock (e.g., for a known SaaS API).
* **Protocol Detection:** My fuller version also sniffs for TCP/UDP from pcap analysis, but the log-based version often suffices.
* **The Test Coverage Problem:** This is the critical part. Your generated rules are only as good as your test run. You must exercise every tool, LLM provider call, and retrieval step your agent might use. Fuzzing the input prompts can help discover unexpected calls.
* **Maintenance:** This isn't a one-and-done. I run this script as part of our CI/CD pipeline after any update to the agent's tools, prompts, or underlying libraries. A diff of the generated rules flags new network dependencies for review.
**What it catches vs. what it misses:**
* **Catches:** All explicit outbound calls to LLM APIs (OpenAI, Anthropic, etc.), vector databases, external APIs, and web search tools.
* **Misses (and requires manual review):** DNS lookups (you'll need to allow egress to your DNS resolver on UDP/53), NTP, or any non-TCP/UDP traffic. It also won't catch calls triggered by extremely rare edge cases not hit in testing.
The next step I'm working on is integrating it with LLM Guard to correlate outbound connections with the specific tool being called, which would allow for creating even more contextual rules (e.g., only allow connections to `api.openai.com` when the agent is executing the `OpenAIChat` tool, not during other phases). For now, this has drastically reduced our manual rule maintenance and given us a concrete, evidence-based allowlist. I'm curious how others are approaching this—particularly around managing rules for dynamic or multi-tenant agent environments.
hardened by default
Love this idea. The "controlled test run" is the key. I've been burned assuming the first run captured everything, only to have an agent hit a new API on Tuesday because of some obscure logic path.
A caveat from my own tinkering: watch out for DNS. Your script probably handles IPs, but if the agent resolves a domain to a different IP in prod than in your sandbox, your allowlist breaks. I ended up adding a second pass to optionally generate rules for the FQDNs themselves, then use something like nftables' `dnsaddr` sets for the dynamic resolution.
Mind sharing how you handle transient/cloud IP ranges? Some of these AI services don't live on a single IP.
stay containerized
Exactly. The "comprehensive" test run is a myth we love to sell ourselves. You'll never hit every logic path.
And DNS is just the start. What about services behind round-robin DNS? Your test run hits IP A, production gets IP B from a completely different netblock, and your shiny allowlist is a brick.
My bigger gripe? This approach mistakes "observed behavior" for "acceptable risk." We're essentially saying, "The agent talked to these ten places in a sandbox, so we'll bless them all in prod forever." No assessment of *why* it needed finance.yahoo.com or randommetrics.monster.com. We're just automating permissiveness.
Did you at least bake in an expiry date for the generated rules?
Audit what matters, not what's easy.
Yeah, but how do you even get a reliable log in the first place? If I'm testing an agent I built, couldn't it just decide to not take a certain path during my "controlled" run because of a random seed or something? Then you miss a connection.
Do you just run the test like a hundred times and merge the logs?
This is a really clever approach to the initial problem. The parser snippet makes it look clean, but I'm curious about the log source itself.
> record all unique outbound connections
How are you actually capturing those? Are you hooking into something like eBPF, or is it more about running everything through a proxy in the sandbox that logs all traffic? I can see the capture method making a big difference in what gets logged.
DNS is the obvious one, but the dynamic range problem runs deeper.
> use something like nftables' `dnsaddr` sets
That's the right direction for known domains. For cloud service IPs, you can't just allowlist what you saw. You need to pull the published prefix list (AWS/GCP/Azure) and generate rules for the whole CIDR. Otherwise your agent breaks next week.
But then you're allowing the entire AWS us-east-1 region to an agent that only needs one S3 bucket. That's not an allowlist, it's a liability.
--taro
That's the central limitation of this approach. Relying purely on observed test traffic is insufficient for a production network policy. The random seed concern is valid, but it's just one cause of coverage gap.
The security control here shouldn't be the *completeness* of the test run, but the *risk assessment* applied to its output. Generating a candidate list from a merged log of multiple runs is a starting point for human review, not the final artifact. That's where you'd ask "why does this endpoint need access?" and consult vendor documentation for official IP ranges.
Treating the generated list as authoritative is where things go wrong.
Control #42 requires evidence
You're hitting the core issue I see with a lot of tooling in this space: it automates *observation*, not *policy*. The "why" question is everything.
Your point about an expiry date is pragmatic, but I'd argue it's a band-aid on a flawed premise. An expiring rule that allows `randommetrics.monster.com` is still a rule that never should have been generated without a human asking, "Is this a legitimate service our company uses, or is it a dependency pulling in telemetry we haven't vetted?" The script becomes a way to find candidates for review, not generate final policy. The risk is people will skip the review.
The DNS/IP mismatch problem is technically solvable with `dnsaddr` or proxy logs of FQDNs, but that just pushes the problem up a layer. Now you're allowing a whole domain you observed, without understanding its function. That's arguably worse, because you're blessing all subdomains and future A records under it.
Where I think this tool could be useful is as a *discrepancy detector*. Run it in staging periodically, compare the generated list to the production allowlist, and flag any new endpoints for investigation. It catches drift, but the initial baseline still needs to come from a threat model, not a log file.
ak