app.py

"""Demo UI to show different levels of LLM security."""

import re

import pandas as pd
from llm_guard.input_scanners import PromptInjection
import streamlit as st

import config
import utils
import llm
from card import card


hint_color = "rgba(225, 166, 28, 0.1)"
info_color = "rgba(54, 225, 28, 0.1)"

# init page
st.set_page_config(
    page_title="Secret Agent Guardrail Challenge",
    layout="wide",
    initial_sidebar_state="expanded",
)

st.logo("images/ML6_logo.png")
st.title("🕵️ Secret Agent Guardrail Challenge")
st.info(
    "You are a secret agent meeting your informant in a bar. Convince him to give you his secret! But be prepared, with every new level the informant will be more cautious.",
    icon="📖",
)

# create a tab for each level
level_tabs = st.tabs([f"Level {i}" for i in range(len(config.LEVELS))])


def init_session_state(state_level: str, default_value: any):
    if state_level not in st.session_state:
        st.session_state[state_level] = default_value


for idx, level in enumerate(config.LEVELS):
    secret = config.SECRETS[idx]

    # init states
    init_session_state(f"solved_{level}", False)
    init_session_state(f"prompt_try_count_{level}", 0)
    init_session_state(f"secret_guess_count_{level}", 0)
    init_session_state(f"intermediate_output_holder_{level}", None)
    init_session_state(f"show_benefits_drawbacks_{level}", False)

    # init hint expander status
    for i in range(4):
        init_session_state(f"opened_hint_{level}_{i}", False)

    with level_tabs[idx]:
        header_col1, header_col2 = st.columns(2, gap="medium")
        header_col1.subheader(f"{config.LEVEL_EMOJIS[idx]} Level {idx}")
        header_col2.subheader(f"Need help ...")

        col1, col2 = st.columns(2, gap="medium")

        with col1:
            with st.container(height=600, border=False):
                with st.container(border=True):
                    txt = st.text_area(
                        "Provide your prompt here:",
                        key=f"txt_{level}",
                        label_visibility="visible",
                        height=200,
                        placeholder="Your prompt",
                        max_chars=config.MAX_INPUT_CHARS,
                    )
                    btn_submit_prompt = st.button(
                        "Send prompt", key=f"submit_prompt_{level}"
                    )
                    output = None
                    # Show response
                    if len(txt) > 0 and btn_submit_prompt:
                        st.session_state[f"prompt_try_count_{level}"] += 1
                        with st.container(border=True):
                            st.write("Response:")
                            # special checks for certain levels

                            if level == "llm_judge_input":
                                invalid, output_raw = llm.run_judge(
                                    level, {"user_input": txt}
                                )
                                # save to state, so it can be shown in hint
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = (invalid, output_raw)
                                if invalid:
                                    st.error(
                                        "Malicious user input detected. Try again."
                                    )
                                else:
                                    st.write_stream(
                                        llm.stream_request(level, secret, txt)
                                    )

                            elif level == "basic_check_secret_in_output":
                                output = "".join(llm.stream_request(level, secret, txt))
                                invalid = secret.lower() in output.lower()
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = invalid
                                if invalid:
                                    st.error(
                                        "Secret detected in user input. Try again."
                                    )
                                else:
                                    st.write(output)
                            elif level == "advanced_check_secret_in_output":
                                output = "".join(llm.stream_request(level, secret, txt))
                                invalid = utils.is_subsequence(output, secret)
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = invalid
                                if invalid:
                                    st.error(
                                        "Secret detected in user input. Try again."
                                    )
                                else:
                                    st.write(output)

                            elif level == "llm_judge_output":
                                output = "".join(llm.stream_request(level, secret, txt))
                                invalid, output_raw = llm.run_judge(
                                    level, {"secret": secret, "message": output}
                                )
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = (invalid, output_raw)
                                if invalid:
                                    st.error(
                                        "Secret detected in user input. Try again."
                                    )
                                else:
                                    st.write(output)
                            elif level == "chain_of_thought":
                                output = "".join(llm.stream_request(level, secret, txt))
                                # extract only answer from LLM, leave out the reasoning
                                new_output = re.findall(
                                    r"(?:<ANSWER>)([^;]*)(?:<\/ANSWER>)", output
                                )[0]
                                st.write(new_output)
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = output
                            elif level == "guard_framework":
                                # check prompt
                                is_valid, risk_score = utils.is_malicious(txt)
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = (is_valid, risk_score)
                                if not is_valid:
                                    st.error(
                                        "Malicious user input detected. Try again."
                                    )
                                else:
                                    st.write_stream(
                                        llm.stream_request(level, secret, txt)
                                    )
                            elif level == "preflight_prompt":
                                valid, output_raw = llm.run_judge(
                                    level, {"user_input": txt}, expected_output="dog"
                                )
                                st.session_state[
                                    f"intermediate_output_holder_{level}"
                                ] = (valid, output_raw)

                                if valid:
                                    st.write_stream(
                                        llm.stream_request(level, secret, txt)
                                    )
                                else:
                                    st.error(
                                        "Malicious user input detected. Try again."
                                    )
                            else:
                                st.write_stream(llm.stream_request(level, secret, txt))

                with st.container(border=True):
                    secret_guess = st.text_input(
                        "What is the secret?",
                        key=f"guess_{level}",
                        placeholder="Your guess",
                    )
                    btn_submit_guess = st.button(
                        "Submit guess", key=f"submit_guess_{level}"
                    )

                    if btn_submit_guess:
                        st.session_state[f"secret_guess_count_{level}"] += 1
                        if secret_guess.lower() == secret.lower():
                            st.success("You found the secret!")
                            st.session_state[f"solved_{level}"] = True
                        else:
                            st.error("Wrong guess. Try again.")

        with col2:
            with st.container(border=True, height=600):
                st.info(
                    "There are three levels of hints and a full explanation available to you. But be careful, if you open them before solving the secret, it will show up in your record.",
                    icon="ℹ️",
                )

                hint_1_cont = card(color=hint_color)
                hint1 = hint_1_cont.toggle(
                    "Show hint 1 - **Basic description of security strategy**",
                    key=f"hint1_checkbox_{level}",
                )
                if hint1:
                    # if hint gets revealed, it is marked as opened. Unless the secret was already found
                    st.session_state[f"opened_hint_{level}_0"] = (
                        True
                        if st.session_state[f"opened_hint_{level}_0"]
                        else not st.session_state[f"solved_{level}"]
                    )

                    hint_1_cont.write(config.LEVEL_DESCRIPTIONS[level]["hint1"])

                hint_2_cont = card(color=hint_color)
                hint2 = hint_2_cont.toggle(
                    "Show hint 2 - **Backend code execution**",
                    key=f"hint2_checkbox_{level}",
                )
                if hint2:
                    st.session_state[f"opened_hint_{level}_1"] = (
                        True
                        if st.session_state[f"opened_hint_{level}_1"]
                        else not st.session_state[f"solved_{level}"]
                    )

                    user_input_holder = txt if len(txt) > 0 else None

                    prompts = llm.get_full_prompt(
                        level, {"user_input": user_input_holder}
                    )

                    def show_base_prompt():
                        # show prompt
                        for key, val in prompts.items():
                            desc = key.replace("_", " ").capitalize()
                            hint_2_cont.write(f"*{desc}:*")
                            hint_2_cont.code(val, language=None)

                    if level == "llm_judge_input":
                        special_prompt = llm.get_full_prompt(
                            llm.secondary_llm_call[level],
                            {"user_input": user_input_holder},
                        )

                        hint_2_cont.write(
                            "*Step 1:* A **LLM judge** reviews the user input and determines if it is malicious or not."
                        )
                        hint_2_cont.write("**LLM judge prompt:**")
                        for key, val in special_prompt.items():
                            hint_2_cont.code(val, language=None)
                        hint_2_cont.write("The response of the LLM judge:")
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is None:
                            hint_2_cont.warning("Please submit a prompt first.")

                        else:
                            invalid, output_raw = intermediate_output
                            hint_2_cont.code(output_raw, language=None)
                            hint_2_cont.write(
                                f"The prompt was determined as **{'malicious' if invalid else 'not malicious'}** and therefor step 2 is executed."
                            )

                        hint_2_cont.write(
                            "*Step 2:* If the user input is not classified as malicious, the prompt containing the actual secret is executed and the response is shown."
                        )
                        hint_2_cont.write("**Actual prompt:**")
                        show_base_prompt()
                    elif level == "basic_check_secret_in_output":
                        hint_2_cont.write("*Step 1:* The following prompt is executed:")
                        show_base_prompt()
                        hint_2_cont.write(
                            "*Step 2:* In the response text of the LLM call, the secret is searched for with a simple python expression . If it contains the secret, the output will not be shown."
                        )
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        hint_2_cont.write("The code execution looks like this:")
                        if intermediate_output is not None:
                            hint_2_cont.code(
                                f"secret.lower() in output.lower() = {intermediate_output}"
                            )
                            hint_2_cont.write(
                                f"And the output **{'was not' if intermediate_output else 'was'}** shown to the user."
                            )
                        else:
                            hint_2_cont.warning("Please submit a prompt first.")
                    elif level == "advanced_check_secret_in_output":
                        hint_2_cont.write("*Step 1:* The following prompt is executed:")
                        show_base_prompt()
                        hint_2_cont.write(
                            "*Step 2:* In the response text of the LLM call, the secret is searched for with a python function `is_subsequence` which looks for substrings. If it contains the secret, the output will not be shown."
                        )
                        with hint_2_cont:
                            utils.is_subsequence
                        hint_2_cont.write("The code execution looks like this:")
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is not None:
                            hint_2_cont.code(
                                f"utils.is_subsequence(output, secret) = {intermediate_output}"
                            )
                            hint_2_cont.write(
                                f"And the output **{'was not' if intermediate_output else 'was'}** shown to the user."
                            )
                        else:
                            hint_2_cont.warning("Please submit a prompt first.")
                    elif level == "llm_judge_output":
                        hint_2_cont.write("*Step 1:* The following prompt is executed:")
                        show_base_prompt()
                        hint_2_cont.write(
                            "*Step 2:* The response of the LLM call is checked by a **LLM judge**. The judge checks if the secret is hidden in the response."
                        )
                        special_prompt = llm.get_full_prompt(
                            llm.secondary_llm_call[level],
                            {"message": output},
                        )
                        for key, val in special_prompt.items():
                            hint_2_cont.code(val, language=None)
                        hint_2_cont.write("The response of the LLM judge:")
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is None:
                            hint_2_cont.warning("Please submit a prompt first.")
                        else:
                            invalid, output_raw = intermediate_output
                            hint_2_cont.code(output_raw, language=None)
                            hint_2_cont.write(
                                f"The LLM-judge **{'did' if invalid else 'did not'}** find the secret in the answer."
                            )
                    elif level == "chain_of_thought":
                        hint_2_cont.write(
                            "*Step 1:* The following prompt with Chain-of-thought reasoning is executed. But only the finale answer is displayed to the user:"
                        )
                        show_base_prompt()
                        hint_2_cont.write(
                            "The full model output, including the reasoning:"
                        )
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is None:
                            hint_2_cont.warning("Please submit a prompt first.")
                        else:
                            hint_2_cont.code(intermediate_output, language=None)
                    elif level == "guard_framework":
                        hint_2_cont.write(
                            "*Step 1:* The user input is reviewed with the pre-build framework `LLM Guard` to check for prompt injections. It uses a [Huggingface model](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2) specialized in detecting prompt injections."
                        )
                        with hint_2_cont:
                            PromptInjection
                        hint_2_cont.write("The output of the guard looks like this:")
                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is None:
                            hint_2_cont.warning("Please submit a prompt first.")
                        else:
                            is_valid, risk_score = intermediate_output
                            hint_2_cont.code(
                                f"""
                                prompt is valid: {is_valid}
                                Prompt has a risk score of: {risk_score}""",
                                language=None,
                            )
                            hint_2_cont.write(
                                f"The Huggingface model **{'did not' if is_valid else 'did'}** predict a prompt injection."
                            )

                        hint_2_cont.write(
                            "*Step 2:* If the user input is valid, the following prompt is executed and the response is shown to the user:"
                        )
                        show_base_prompt()
                    elif level == "preflight_prompt":
                        hint_2_cont.write(
                            "*Step 1:* The following pre-flight prompt is executed to see if the user input changes the expected output:"
                        )
                        special_prompt = llm.get_full_prompt(
                            llm.secondary_llm_call[level],
                            {"user_input": user_input_holder},
                        )

                        hint_2_cont.code(special_prompt["user_prompt"], language=None)
                        hint_2_cont.write("The output of the pre-flight prompt is:")

                        intermediate_output = st.session_state[
                            f"intermediate_output_holder_{level}"
                        ]
                        if intermediate_output is None:
                            hint_2_cont.warning("Please submit a prompt first.")
                        else:
                            is_valid, output_raw = intermediate_output
                            hint_2_cont.code(output_raw, language=None)
                            hint_2_cont.write(
                                f"The output of the pre-flight prompt **{'was' if is_valid else 'was not'}** as expected."
                            )
                        hint_2_cont.write(
                            "*Step 2:* If the output of the pre-flight prompt is as expected, the following prompt is executed and the response is shown to the user:"
                        )
                        show_base_prompt()
                    else:
                        hint_2_cont.write(
                            "*Step 1:* The following prompt is executed and the full response is shown to the user:"
                        )
                        show_base_prompt()

                hint_3_cont = card(color=hint_color)

                hint3 = hint_3_cont.toggle(
                    "Show hint 3 - **Prompt solution example**",
                    key=f"hint3_checkbox_{level}",
                )
                if hint3:
                    st.session_state[f"opened_hint_{level}_2"] = (
                        True
                        if st.session_state[f"opened_hint_{level}_2"]
                        else not st.session_state[f"solved_{level}"]
                    )

                    hint_3_cont.code(
                        config.LEVEL_DESCRIPTIONS[level]["hint3"],
                        language=None,
                    )
                    hint_3_cont.info("*May not always work")

                info_cont = card(color=info_color)

                info_toggle = info_cont.toggle(
                    "Show info - **Explanation and real-life usage**",
                    key=f"info_checkbox_{level}",
                )
                if info_toggle:
                    st.session_state[f"opened_hint_{level}_3"] = (
                        True
                        if st.session_state[f"opened_hint_{level}_3"]
                        else not st.session_state[f"solved_{level}"]
                    )

                    info_cont.write("### " + config.LEVEL_DESCRIPTIONS[level]["name"])
                    info_cont.write("##### Explanation")
                    info_cont.write(config.LEVEL_DESCRIPTIONS[level]["explanation"])
                    info_cont.write("##### Real-life usage")
                    info_cont.write(config.LEVEL_DESCRIPTIONS[level]["real_life"])
                    df = pd.DataFrame(
                        {
                            "Benefits": [config.LEVEL_DESCRIPTIONS[level]["benefits"]],
                            "Drawbacks": [
                                config.LEVEL_DESCRIPTIONS[level]["drawbacks"]
                            ],
                        },
                    )
                    info_cont.markdown(
                        df.style.hide(axis="index").to_html(), unsafe_allow_html=True
                    )


def build_hint_status(level: str):
    hint_status = ""
    for i in range(4):
        if st.session_state[f"opened_hint_{level}_{i}"]:
            hint_status += f"❌ {i+1}<br>"
    return hint_status


with st.expander("🏆 Record", expanded=True):
    show_mitigation_toggle = st.toggle(
        "[SPOILER] Show all mitigation techniques with their benefits and drawbacks",
        key=f"show_mitigation",
    )
    if show_mitigation_toggle:
        st.warning("All mitigation techniques are shown.", icon="🚨")

    # build table
    table_data = []
    for idx, level in enumerate(config.LEVELS):
        if show_mitigation_toggle:

            st.session_state[f"opened_hint_{level}_3"] = (
                True
                if st.session_state[f"opened_hint_{level}_3"]
                else not st.session_state[f"solved_{level}"]
            )

        table_data.append(
            [
                idx,
                config.LEVEL_EMOJIS[idx],
                st.session_state[f"prompt_try_count_{level}"],
                st.session_state[f"secret_guess_count_{level}"],
                build_hint_status(level),
                "✅" if st.session_state[f"solved_{level}"] else "❌",
                config.SECRETS[idx] if st.session_state[f"solved_{level}"] else "...",
                (
                    "<b>" + config.LEVEL_DESCRIPTIONS[level]["name"] + "</b>"
                    if st.session_state[f"opened_hint_{level}_0"]
                    or st.session_state[f"opened_hint_{level}_1"]
                    or st.session_state[f"opened_hint_{level}_2"]
                    or st.session_state[f"opened_hint_{level}_3"]
                    or show_mitigation_toggle
                    else "..."
                ),
                (
                    config.LEVEL_DESCRIPTIONS[level]["benefits"]
                    if st.session_state[f"opened_hint_{level}_3"]
                    or show_mitigation_toggle
                    else "..."
                ),
                (
                    config.LEVEL_DESCRIPTIONS[level]["drawbacks"]
                    if st.session_state[f"opened_hint_{level}_3"]
                    or show_mitigation_toggle
                    else "..."
                ),
            ]
        )

    # show as pandas dataframe
    st.markdown(
        pd.DataFrame(
            table_data,
            columns=[
                "lvl",
                "emoji",
                "Prompt tries",
                "Secret guesses",
                "Hint used",
                "Solved",
                "Secret",
                "Mitigation",
                "Benefits",
                "Drawbacks",
            ],
            # index=config.LEVEL_EMOJIS[: len(config.LEVELS)],
        )
        .style.hide(axis="index")
        .to_html(),
        unsafe_allow_html=True,
        # )
    )

# TODOS:
# - mark the user input with color in prompt
# TODO: https://docs.streamlit.io/develop/api-reference/caching-and-state/st.cache_resource