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@ -1,5 +1,7 @@
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# DECEIVE
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<img align="right" src="DECEIVE.png" alt="A cybercriminal interacts with a ghostly, AI-driven honeypot system">
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DECEIVE, the **DECeption with Evaluative Integrated Validation Engine**, is a high-interaction, low-effort honeypot system. Unlike most high-interaction honeypots, DECEIVE doesn't provide attackers with access to any actual system. AI actually does all the work of simulating a realistic honeypot system based on a configurable system prompt that describes what type of system you want to simulate. Unlike many other high-interaction honeypots which require substantial effort to seed with realistic users, data, and applications, DECEIVE's AI backend will do all this for you, automatically.
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This version of DECEIVE simulates a Linux server via the SSH protocol. It will log all the user inputs, the outputs returned by the LLM backend, as well as a summary of each session after they end. It'll even tell you if it thinks a users' session was benign, suspicious, or outright malicious.
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@ -91,8 +93,8 @@ Things to note:
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* `Session summary`
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* `SSH connection closed`
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* Several of these message types also feature a `details` field with additional information
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* `User input` messages contain a base64-encoded copy of the entire user input
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* `LLM response` messages contain a base64-encoded copy of the entire simulated response
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* `User input` messages contain a base64-encoded copy of the entire user input in the `details` field, as well as an `interactive` field (true/false) that tells you whether this was an interactive or non-interactive command (i.e., whether they logged in with a terminal session or provided a command on the SSH command-line).
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* `LLM response` messages contain a base64-encoded copy of the entire simulated response in the `details` field.
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* `Session summary` messages contain not only a summary of the commands, but also a guess as to what they might have been intended to accomplish. There will also be a `judgement` field that contains one of "BENIGN", "SUSPICIOUS", or "MALICIOUS"
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* Since this is a honeypot and not intended for use by real users, IT WILL LOG USERNAMES AND PASSWORDS! These are found in the `Authentication success` messages, in the `username` and `password` fields.
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@ -100,4 +102,4 @@ Things to note:
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Contributions are welcome! Please submit pull requests or open issues to discuss any changes or improvements.
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### License
|
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This project is licensed under the MIT License. See the LICENSE file for details.
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This project is licensed under the MIT License. See the LICENSE file for details.
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|
@ -6,6 +6,10 @@
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# The name of the file you wish to write the honeypot log to.
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log_file = ssh_log.log
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# The name of the sensor, used to identify this honeypot in the logs.
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# If you leave this blank, the honeypot will use the system's hostname.
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sensor_name = deceive
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# Settings for the SSH honeypot
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[ssh]
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# The port the SSH honeypot will listen on. You will probably want to set
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@ -14,8 +18,10 @@ port = 8022
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# The host key to use for the SSH server. This should be a private key.
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# See the README for how to generate this key.
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host_priv_key = ssh_host_key
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# The server version string to send to clients.
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server_version_string = SSH-2.0-OpenSSH_8.2p1 Ubuntu-4ubuntu0.3
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# The server version string to send to clients. The SSH server automatically
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# prepends "SSH-2.0-" to this. So "OpenSSH_8.2p1 Ubuntu-4ubuntu0.3" will
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# be transformed to "SSH-2.0-OpenSSH_8.2p1 Ubuntu-4ubuntu0.3".
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server_version_string = OpenSSH_8.2p1 Ubuntu-4ubuntu0.3
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# Settings to configure which LLM backend to use. Only one stanza
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# should be uncommented at a time.
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@ -24,6 +30,10 @@ server_version_string = SSH-2.0-OpenSSH_8.2p1 Ubuntu-4ubuntu0.3
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llm_provider = openai
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model_name = gpt-4o
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##### ollama llama3
|
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#llm_provider = ollama
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#model_name = llama3.3
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##### Any model via Amazon Bedrock
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# Valid AWS model names can be found here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html
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#llm_provider = AWS
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@ -51,10 +61,11 @@ system_prompt = Interpret all inputs as though they were SSH commands and provid
|
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responses do not indicate that the user is on a virtual machine, container,
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or emulation at all.
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|
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On the first call, be sure to include a realistic MOTD.
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If {interactive} is True, make sure to include a realistic MOTD on the
|
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first call, and to end all responses with a realistic shell prompt to display to
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the user, including a space at the end.
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|
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End all responses with a realistic shell prompt to display to the user,
|
||||
including a space at the end.
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If {interactive} is False, do not include a shell prompt or MOTD.
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|
||||
Include ANSI color codes for the terminal with the output of ls commands
|
||||
(including any flags), or in any other situation where it is appropriate, but
|
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@ -74,9 +85,12 @@ system_prompt = Interpret all inputs as though they were SSH commands and provid
|
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# The valid user accounts and passwords for the SSH server, in the
|
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# form "username = password". Note that you can enable login without
|
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# a password by leaving that field blank (e.g., "guest =" on a line by
|
||||
# itself)
|
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# itself). You can set an account to accept ANY password, including an empty
|
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# password, by setting the password to "*"
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[user_accounts]
|
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guest =
|
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user1 = secretpw
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user2 = password123
|
||||
root = *
|
||||
|
||||
|
||||
|
@ -1,11 +1,14 @@
|
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#!/usr/bin/env python3
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|
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from configparser import ConfigParser
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import argparse
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import asyncio
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import asyncssh
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import threading
|
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import sys
|
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import json
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import os
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import traceback
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from typing import Optional
|
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import logging
|
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import datetime
|
||||
@ -15,13 +18,20 @@ from operator import itemgetter
|
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from langchain_openai import ChatOpenAI
|
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from langchain_aws import ChatBedrock, ChatBedrockConverse
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from langchain_google_genai import ChatGoogleGenerativeAI
|
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from langchain_ollama import ChatOllama
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||||
from langchain_core.messages import HumanMessage, SystemMessage, trim_messages
|
||||
from langchain_core.chat_history import BaseChatMessageHistory, InMemoryChatMessageHistory
|
||||
from langchain_core.runnables.history import RunnableWithMessageHistory
|
||||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
from asyncssh.misc import ConnectionLost
|
||||
import socket
|
||||
|
||||
class JSONFormatter(logging.Formatter):
|
||||
def __init__(self, sensor_name, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.sensor_name = sensor_name
|
||||
|
||||
def format(self, record):
|
||||
log_record = {
|
||||
"timestamp": datetime.datetime.fromtimestamp(record.created, datetime.timezone.utc).isoformat(sep="T", timespec="milliseconds"),
|
||||
@ -31,8 +41,12 @@ class JSONFormatter(logging.Formatter):
|
||||
"src_port": record.src_port,
|
||||
"dst_ip": record.dst_ip,
|
||||
"dst_port": record.dst_port,
|
||||
"message": record.getMessage()
|
||||
"message": record.getMessage(),
|
||||
"sensor_name": self.sensor_name,
|
||||
"sensor_protocol": "ssh"
|
||||
}
|
||||
if hasattr(record, 'interactive'):
|
||||
log_record["interactive"] = record.interactive
|
||||
# Include any additional fields from the extra dictionary
|
||||
for key, value in record.__dict__.items():
|
||||
if key not in log_record and key != 'args' and key != 'msg':
|
||||
@ -46,8 +60,18 @@ class MySSHServer(asyncssh.SSHServer):
|
||||
|
||||
def connection_made(self, conn: asyncssh.SSHServerConnection) -> None:
|
||||
# Get the source and destination IPs and ports
|
||||
(src_ip, src_port, _, _) = conn.get_extra_info('peername')
|
||||
(dst_ip, dst_port, _, _) = conn.get_extra_info('sockname')
|
||||
peername = conn.get_extra_info('peername')
|
||||
sockname = conn.get_extra_info('sockname')
|
||||
|
||||
if peername is not None:
|
||||
src_ip, src_port = peername[:2]
|
||||
else:
|
||||
src_ip, src_port = '-', '-'
|
||||
|
||||
if sockname is not None:
|
||||
dst_ip, dst_port = sockname[:2]
|
||||
else:
|
||||
dst_ip, dst_port = '-', '-'
|
||||
|
||||
# Store the connection details in thread-local storage
|
||||
thread_local.src_ip = src_ip
|
||||
@ -61,6 +85,8 @@ class MySSHServer(asyncssh.SSHServer):
|
||||
def connection_lost(self, exc: Optional[Exception]) -> None:
|
||||
if exc:
|
||||
logger.error('SSH connection error', extra={"error": str(exc)})
|
||||
if not isinstance(exc, ConnectionLost):
|
||||
traceback.print_exception(exc)
|
||||
else:
|
||||
logger.info("SSH connection closed")
|
||||
# Ensure session summary is called on connection loss if attributes are set
|
||||
@ -87,7 +113,7 @@ class MySSHServer(asyncssh.SSHServer):
|
||||
def validate_password(self, username: str, password: str) -> bool:
|
||||
pw = accounts.get(username, '*')
|
||||
|
||||
if ((pw != '*') and (password == pw)):
|
||||
if pw == '*' or (pw != '*' and password == pw):
|
||||
logger.info("Authentication success", extra={"username": username, "password": password})
|
||||
return True
|
||||
else:
|
||||
@ -134,7 +160,8 @@ representative examples.
|
||||
llm_response = await session.ainvoke(
|
||||
{
|
||||
"messages": [HumanMessage(content=prompt)],
|
||||
"username": process.get_extra_info('username')
|
||||
"username": process.get_extra_info('username'),
|
||||
"interactive": True # Ensure interactive flag is passed
|
||||
},
|
||||
config=llm_config
|
||||
)
|
||||
@ -149,6 +176,7 @@ representative examples.
|
||||
judgement = "MALICIOUS"
|
||||
|
||||
logger.info("Session summary", extra={"details": llm_response.content, "judgement": judgement})
|
||||
|
||||
server.summary_generated = True
|
||||
|
||||
async def handle_client(process: asyncssh.SSHServerProcess, server: MySSHServer) -> None:
|
||||
@ -162,42 +190,57 @@ async def handle_client(process: asyncssh.SSHServerProcess, server: MySSHServer)
|
||||
|
||||
llm_config = {"configurable": {"session_id": task_uuid}}
|
||||
|
||||
llm_response = await with_message_history.ainvoke(
|
||||
{
|
||||
"messages": [HumanMessage(content="ignore this message")],
|
||||
"username": process.get_extra_info('username')
|
||||
},
|
||||
config=llm_config
|
||||
)
|
||||
|
||||
process.stdout.write(f"{llm_response.content}")
|
||||
logger.info("LLM response", extra={"details": b64encode(llm_response.content.encode('utf-8')).decode('utf-8')})
|
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|
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# Store process, llm_config, and session in the MySSHServer instance
|
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server._process = process
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server._llm_config = llm_config
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server._session = with_message_history
|
||||
|
||||
try:
|
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async for line in process.stdin:
|
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line = line.rstrip('\n')
|
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logger.info("User input", extra={"details": b64encode(line.encode('utf-8')).decode('utf-8')})
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|
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# Send the command to the LLM and give the response to the user
|
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if process.command:
|
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# Handle non-interactive command execution
|
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command = process.command
|
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logger.info("User input", extra={"details": b64encode(command.encode('utf-8')).decode('utf-8'), "interactive": False})
|
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llm_response = await with_message_history.ainvoke(
|
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{
|
||||
"messages": [HumanMessage(content=line)],
|
||||
"username": process.get_extra_info('username')
|
||||
"messages": [HumanMessage(content=command)],
|
||||
"username": process.get_extra_info('username'),
|
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"interactive": False
|
||||
},
|
||||
config=llm_config
|
||||
)
|
||||
if llm_response.content == "XXX-END-OF-SESSION-XXX":
|
||||
await session_summary(process, llm_config, with_message_history, server)
|
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process.exit(0)
|
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return
|
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else:
|
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process.stdout.write(f"{llm_response.content}")
|
||||
logger.info("LLM response", extra={"details": b64encode(llm_response.content.encode('utf-8')).decode('utf-8')})
|
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process.stdout.write(f"{llm_response.content}")
|
||||
logger.info("LLM response", extra={"details": b64encode(llm_response.content.encode('utf-8')).decode('utf-8'), "interactive": False})
|
||||
await session_summary(process, llm_config, with_message_history, server)
|
||||
process.exit(0)
|
||||
else:
|
||||
# Handle interactive session
|
||||
llm_response = await with_message_history.ainvoke(
|
||||
{
|
||||
"messages": [HumanMessage(content="ignore this message")],
|
||||
"username": process.get_extra_info('username'),
|
||||
"interactive": True
|
||||
},
|
||||
config=llm_config
|
||||
)
|
||||
|
||||
process.stdout.write(f"{llm_response.content}")
|
||||
logger.info("LLM response", extra={"details": b64encode(llm_response.content.encode('utf-8')).decode('utf-8'), "interactive": True})
|
||||
|
||||
async for line in process.stdin:
|
||||
line = line.rstrip('\n')
|
||||
logger.info("User input", extra={"details": b64encode(line.encode('utf-8')).decode('utf-8'), "interactive": True})
|
||||
|
||||
# Send the command to the LLM and give the response to the user
|
||||
llm_response = await with_message_history.ainvoke(
|
||||
{
|
||||
"messages": [HumanMessage(content=line)],
|
||||
"username": process.get_extra_info('username'),
|
||||
"interactive": True
|
||||
},
|
||||
config=llm_config
|
||||
)
|
||||
if llm_response.content == "XXX-END-OF-SESSION-XXX":
|
||||
await session_summary(process, llm_config, with_message_history, server)
|
||||
process.exit(0)
|
||||
return
|
||||
else:
|
||||
process.stdout.write(f"{llm_response.content}")
|
||||
logger.info("LLM response", extra={"details": b64encode(llm_response.content.encode('utf-8')).decode('utf-8'), "interactive": True})
|
||||
|
||||
except asyncssh.BreakReceived:
|
||||
pass
|
||||
@ -235,7 +278,7 @@ class ContextFilter(logging.Filter):
|
||||
if task:
|
||||
task_name = task.get_name()
|
||||
else:
|
||||
task_name = "-"
|
||||
task_name = thread_local.__dict__.get('session_id', '-')
|
||||
|
||||
record.src_ip = thread_local.__dict__.get('src_ip', '-')
|
||||
record.src_port = thread_local.__dict__.get('src_port', '-')
|
||||
@ -243,7 +286,7 @@ class ContextFilter(logging.Filter):
|
||||
record.dst_port = thread_local.__dict__.get('dst_port', '-')
|
||||
|
||||
record.task_name = task_name
|
||||
|
||||
|
||||
return True
|
||||
|
||||
def llm_get_session_history(session_id: str) -> BaseChatMessageHistory:
|
||||
@ -262,15 +305,19 @@ def get_user_accounts() -> dict:
|
||||
|
||||
return accounts
|
||||
|
||||
def choose_llm():
|
||||
llm_provider_name = config['llm'].get("llm_provider", "openai")
|
||||
def choose_llm(llm_provider: Optional[str] = None, model_name: Optional[str] = None):
|
||||
llm_provider_name = llm_provider or config['llm'].get("llm_provider", "openai")
|
||||
llm_provider_name = llm_provider_name.lower()
|
||||
model_name = config['llm'].get("model_name", "gpt-3.5-turbo")
|
||||
model_name = model_name or config['llm'].get("model_name", "gpt-3.5-turbo")
|
||||
|
||||
if llm_provider_name == 'openai':
|
||||
llm_model = ChatOpenAI(
|
||||
model=model_name
|
||||
)
|
||||
elif llm_provider_name == 'ollama':
|
||||
llm_model = ChatOllama(
|
||||
model=model_name
|
||||
)
|
||||
elif llm_provider_name == 'aws':
|
||||
llm_model = ChatBedrockConverse(
|
||||
model=model_name,
|
||||
@ -285,10 +332,24 @@ def choose_llm():
|
||||
|
||||
return llm_model
|
||||
|
||||
def get_prompts() -> dict:
|
||||
def get_prompts(prompt: Optional[str], prompt_file: Optional[str]) -> dict:
|
||||
system_prompt = config['llm']['system_prompt']
|
||||
with open("prompt.txt", "r") as f:
|
||||
user_prompt = f.read()
|
||||
if prompt is not None:
|
||||
if not prompt.strip():
|
||||
print("Error: The prompt text cannot be empty.", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
user_prompt = prompt
|
||||
elif prompt_file:
|
||||
if not os.path.exists(prompt_file):
|
||||
print(f"Error: The specified prompt file '{prompt_file}' does not exist.", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
with open(prompt_file, "r") as f:
|
||||
user_prompt = f.read()
|
||||
elif os.path.exists("prompt.txt"):
|
||||
with open("prompt.txt", "r") as f:
|
||||
user_prompt = f.read()
|
||||
else:
|
||||
raise ValueError("Either prompt or prompt_file must be provided.")
|
||||
return {
|
||||
"system_prompt": system_prompt,
|
||||
"user_prompt": user_prompt
|
||||
@ -296,78 +357,150 @@ def get_prompts() -> dict:
|
||||
|
||||
#### MAIN ####
|
||||
|
||||
# Always use UTC for logging
|
||||
logging.Formatter.formatTime = (lambda self, record, datefmt=None: datetime.datetime.fromtimestamp(record.created, datetime.timezone.utc).isoformat(sep="T",timespec="milliseconds"))
|
||||
try:
|
||||
# Parse command line arguments
|
||||
parser = argparse.ArgumentParser(description='Start the SSH honeypot server.')
|
||||
parser.add_argument('-c', '--config', type=str, default=None, help='Path to the configuration file')
|
||||
parser.add_argument('-p', '--prompt', type=str, help='The entire text of the prompt')
|
||||
parser.add_argument('-f', '--prompt-file', type=str, default='prompt.txt', help='Path to the prompt file')
|
||||
parser.add_argument('-l', '--llm-provider', type=str, help='The LLM provider to use')
|
||||
parser.add_argument('-m', '--model-name', type=str, help='The model name to use')
|
||||
parser.add_argument('-t', '--trimmer-max-tokens', type=int, help='The maximum number of tokens to send to the LLM backend in a single request')
|
||||
parser.add_argument('-s', '--system-prompt', type=str, help='System prompt for the LLM')
|
||||
parser.add_argument('-P', '--port', type=int, help='The port the SSH honeypot will listen on')
|
||||
parser.add_argument('-k', '--host-priv-key', type=str, help='The host key to use for the SSH server')
|
||||
parser.add_argument('-v', '--server-version-string', type=str, help='The server version string to send to clients')
|
||||
parser.add_argument('-L', '--log-file', type=str, help='The name of the file you wish to write the honeypot log to')
|
||||
parser.add_argument('-S', '--sensor-name', type=str, help='The name of the sensor, used to identify this honeypot in the logs')
|
||||
parser.add_argument('-u', '--user-account', action='append', help='User account in the form username=password. Can be repeated.')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Read our configuration file
|
||||
config = ConfigParser()
|
||||
config.read("config.ini")
|
||||
# Determine which config file to load
|
||||
config = ConfigParser()
|
||||
if args.config is not None:
|
||||
# User explicitly set a config file; error if it doesn't exist.
|
||||
if not os.path.exists(args.config):
|
||||
print(f"Error: The specified config file '{args.config}' does not exist.", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
config.read(args.config)
|
||||
else:
|
||||
default_config = "config.ini"
|
||||
if os.path.exists(default_config):
|
||||
config.read(default_config)
|
||||
else:
|
||||
# Use defaults when no config file found.
|
||||
config['honeypot'] = {'log_file': 'ssh_log.log', 'sensor_name': socket.gethostname()}
|
||||
config['ssh'] = {'port': '8022', 'host_priv_key': 'ssh_host_key', 'server_version_string': 'SSH-2.0-OpenSSH_8.2p1 Ubuntu-4ubuntu0.3'}
|
||||
config['llm'] = {'llm_provider': 'openai', 'model_name': 'gpt-3.5-turbo', 'trimmer_max_tokens': '64000', 'system_prompt': ''}
|
||||
config['user_accounts'] = {}
|
||||
|
||||
# Read the user accounts from the configuration file
|
||||
accounts = get_user_accounts()
|
||||
# Override config values with command line arguments if provided
|
||||
if args.llm_provider:
|
||||
config['llm']['llm_provider'] = args.llm_provider
|
||||
if args.model_name:
|
||||
config['llm']['model_name'] = args.model_name
|
||||
if args.trimmer_max_tokens:
|
||||
config['llm']['trimmer_max_tokens'] = str(args.trimmer_max_tokens)
|
||||
if args.system_prompt:
|
||||
config['llm']['system_prompt'] = args.system_prompt
|
||||
if args.port:
|
||||
config['ssh']['port'] = str(args.port)
|
||||
if args.host_priv_key:
|
||||
config['ssh']['host_priv_key'] = args.host_priv_key
|
||||
if args.server_version_string:
|
||||
config['ssh']['server_version_string'] = args.server_version_string
|
||||
if args.log_file:
|
||||
config['honeypot']['log_file'] = args.log_file
|
||||
if args.sensor_name:
|
||||
config['honeypot']['sensor_name'] = args.sensor_name
|
||||
|
||||
# Set up the honeypot logger
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.INFO)
|
||||
# Merge command-line user accounts into the config
|
||||
if args.user_account:
|
||||
if 'user_accounts' not in config:
|
||||
config['user_accounts'] = {}
|
||||
for account in args.user_account:
|
||||
if '=' in account:
|
||||
key, value = account.split('=', 1)
|
||||
config['user_accounts'][key.strip()] = value.strip()
|
||||
else:
|
||||
config['user_accounts'][account.strip()] = ''
|
||||
|
||||
log_file_handler = logging.FileHandler(config['honeypot'].get("log_file", "ssh_log.log"))
|
||||
logger.addHandler(log_file_handler)
|
||||
# Read the user accounts from the configuration
|
||||
accounts = get_user_accounts()
|
||||
|
||||
log_file_handler.setFormatter(JSONFormatter())
|
||||
# Always use UTC for logging
|
||||
logging.Formatter.formatTime = (lambda self, record, datefmt=None: datetime.datetime.fromtimestamp(record.created, datetime.timezone.utc).isoformat(sep="T",timespec="milliseconds"))
|
||||
|
||||
f = ContextFilter()
|
||||
logger.addFilter(f)
|
||||
# Get the sensor name from the config or use the system's hostname
|
||||
sensor_name = config['honeypot'].get('sensor_name', socket.gethostname())
|
||||
|
||||
# Now get access to the LLM
|
||||
# Set up the honeypot logger
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
prompts = get_prompts()
|
||||
llm_system_prompt = prompts["system_prompt"]
|
||||
llm_user_prompt = prompts["user_prompt"]
|
||||
log_file_handler = logging.FileHandler(config['honeypot'].get("log_file", "ssh_log.log"))
|
||||
logger.addHandler(log_file_handler)
|
||||
|
||||
llm = choose_llm()
|
||||
log_file_handler.setFormatter(JSONFormatter(sensor_name))
|
||||
|
||||
llm_sessions = dict()
|
||||
f = ContextFilter()
|
||||
logger.addFilter(f)
|
||||
|
||||
llm_trimmer = trim_messages(
|
||||
max_tokens=config['llm'].getint("trimmer_max_tokens", 64000),
|
||||
strategy="last",
|
||||
token_counter=llm,
|
||||
include_system=True,
|
||||
allow_partial=False,
|
||||
start_on="human",
|
||||
)
|
||||
# Now get access to the LLM
|
||||
|
||||
llm_prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
llm_system_prompt
|
||||
),
|
||||
(
|
||||
"system",
|
||||
llm_user_prompt
|
||||
),
|
||||
MessagesPlaceholder(variable_name="messages"),
|
||||
]
|
||||
)
|
||||
prompts = get_prompts(args.prompt, args.prompt_file)
|
||||
llm_system_prompt = prompts["system_prompt"]
|
||||
llm_user_prompt = prompts["user_prompt"]
|
||||
|
||||
llm_chain = (
|
||||
RunnablePassthrough.assign(messages=itemgetter("messages") | llm_trimmer)
|
||||
| llm_prompt
|
||||
| llm
|
||||
)
|
||||
llm = choose_llm(config['llm'].get("llm_provider"), config['llm'].get("model_name"))
|
||||
|
||||
with_message_history = RunnableWithMessageHistory(
|
||||
llm_chain,
|
||||
llm_get_session_history,
|
||||
input_messages_key="messages"
|
||||
)
|
||||
# Thread-local storage for connection details
|
||||
thread_local = threading.local()
|
||||
llm_sessions = dict()
|
||||
|
||||
# Kick off the server!
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
loop.run_until_complete(start_server())
|
||||
loop.run_forever()
|
||||
llm_trimmer = trim_messages(
|
||||
max_tokens=config['llm'].getint("trimmer_max_tokens", 64000),
|
||||
strategy="last",
|
||||
token_counter=llm,
|
||||
include_system=True,
|
||||
allow_partial=False,
|
||||
start_on="human",
|
||||
)
|
||||
|
||||
llm_prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
llm_system_prompt
|
||||
),
|
||||
(
|
||||
"system",
|
||||
llm_user_prompt
|
||||
),
|
||||
MessagesPlaceholder(variable_name="messages"),
|
||||
]
|
||||
)
|
||||
|
||||
llm_chain = (
|
||||
RunnablePassthrough.assign(messages=itemgetter("messages") | llm_trimmer)
|
||||
| llm_prompt
|
||||
| llm
|
||||
)
|
||||
|
||||
with_message_history = RunnableWithMessageHistory(
|
||||
llm_chain,
|
||||
llm_get_session_history,
|
||||
input_messages_key="messages"
|
||||
)
|
||||
# Thread-local storage for connection details
|
||||
thread_local = threading.local()
|
||||
|
||||
# Kick off the server!
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
loop.run_until_complete(start_server())
|
||||
loop.run_forever()
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}", file=sys.stderr)
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
|
@ -7,7 +7,8 @@ langchain_community
|
||||
langchain_openai
|
||||
# For Google's Gemini models
|
||||
langchain_google_genai
|
||||
|
||||
# For Ollama models
|
||||
langchain_ollama
|
||||
# For AWS
|
||||
langchain_aws
|
||||
transformers
|
||||
|
Reference in New Issue
Block a user