fix(llm): bound Ollama requests with a configurable timeout

The Atomic Agents client was built with no timeout, so an Ollama server that
accepted the TCP connection but never replied (most commonly a large model still
loading into VRAM) left the CLI blocked in agent.run() forever. The caller's
`except Exception` handler only ever fired on connection-refused.

- llm.generate_candidates() takes a `timeout` parameter (default
  DEFAULT_TIMEOUT_SECONDS = 300.0) and forwards it to the OpenAI client.
- openai.APITimeoutError is translated into a new domain-level
  llm.LLMTimeoutError so main.py need not import openai itself, keeping the
  atomic-agents/instructor dependency isolated to llm.py as documented.
- hcatOllama passes the new `ollamaTimeout` config value and prints
  timeout-specific guidance (elapsed seconds, VRAM-loading hint, the setting to
  raise) instead of the misleading "ensure Ollama is running" message.
- Documented `ollamaTimeout` in config.json.example and README.

Also closes two test gaps: the defensive `except ValueError` handler in
hcatOllama now has coverage, and the misleadingly-named `test_unknown_mode_raises`
is split into explicit unit tests of _build_request's mode validation. Ticked the
completed steps in the LLM Atomic Agents plan doc.

Co-Authored-By: Claude <noreply@anthropic.com>

CHANGELOG.md

@@ -23,6 +23,15 @@ Dates are omitted for releases predating this file; see the git tags for exact t
   menu: select the LLM attack (option 12), then choose "Wordlist" to derive basewords from
   a sample wordlist.
 
+### Fixed
+
+- **The LLM attack no longer hangs forever waiting on Ollama.** Generation requests are now
+  bounded by a configurable timeout (`ollamaTimeout` in `config.json`, default 300 seconds).
+  Previously, if Ollama accepted the connection but never replied — most commonly a large
+  model still loading into VRAM — hate_crack sat at a frozen prompt with no recourse but
+  Ctrl-C. When the timeout fires you now get a specific message naming the elapsed timeout
+  and the setting to raise, instead of a misleading "ensure Ollama is running" hint.
+
 ### Removed
 
 - **Automatic model pulling.** hate_crack no longer pulls missing Ollama models; pull them

README.md

@@ -459,17 +459,19 @@ Set Hashview credentials in `config.json`:
 
 #### Ollama Configuration
 
-The LLM Attack (option 12) uses Ollama to generate password candidates. Configure the model and context window in `config.json`:
+The LLM Attack (option 12) uses Ollama to generate password candidates. Configure the model, context window, and request timeout in `config.json`:
 
 ```json
 {
   "ollamaModel": "qwen2.5:32b",
-  "ollamaNumCtx": 2048
+  "ollamaNumCtx": 2048,
+  "ollamaTimeout": 300
 }
 ```
 
 - **`ollamaModel`** — The Ollama model used for candidate generation (default: `qwen2.5:32b`). The LLM attack uses structured (JSON) output, so choose a model with good tool/JSON support.
 - **`ollamaNumCtx`** — Context window size for the model (default: `2048`).
+- **`ollamaTimeout`** — Seconds to wait for a generation response before giving up (default: `300`). Raise this if a large model is still loading into VRAM on the first request, which can otherwise exceed the timeout; hate_crack prints the elapsed timeout and this setting's name when it fires.
 - The Ollama URL defaults to `http://localhost:11434` (override via the `OLLAMA_HOST` env var). Ensure Ollama is running and the model is pulled (`ollama pull qwen2.5:32b`) before using the LLM Attack — hate_crack no longer auto-pulls missing models.
 
 The attack offers two generation modes: **Target info** (company / industry / location) and **Wordlist** (derive denylist basewords from a sample wordlist).

config.json.example

@@ -27,6 +27,7 @@
   "hashmob_api_key": "",
   "ollamaModel": "qwen2.5:32b",
   "ollamaNumCtx": 2048,
+  "ollamaTimeout": 300,
   "omenTrainingList": "rockyou.txt",
   "omenMaxCandidates": 50000000,
   "pcfgRuleset": "DEFAULT",

hate_crack/llm.py

@@ -5,13 +5,22 @@ to this module only through ``generate_candidates``.
 """
 
 import instructor
-from openai import OpenAI
+from openai import APITimeoutError, OpenAI
 from pydantic import Field
 
 from atomic_agents import AgentConfig, AtomicAgent, BaseIOSchema
 from atomic_agents.context import SystemPromptGenerator
 
 MAX_CANDIDATE_LEN = 128
+DEFAULT_TIMEOUT_SECONDS = 300.0
+
+
+class LLMTimeoutError(Exception):
+    """The LLM server accepted the request but did not respond in time.
+
+    Raised instead of ``openai.APITimeoutError`` so callers do not need to import
+    ``openai`` themselves.
+    """
 
 
 class GenerationInput(BaseIOSchema):
@@ -93,17 +102,23 @@ def generate_candidates(
     num_ctx: int,
     mode: str,
     context_data: dict,
+    timeout: float = DEFAULT_TIMEOUT_SECONDS,
 ) -> list[str]:
     """Generate password candidates via an Ollama-backed AtomicAgent.
 
+    ``timeout`` is the number of seconds to wait for a generation response before
+    giving up; it bounds the whole request so a server that accepts the
+    connection but never replies (e.g. a large model still loading into VRAM)
+    cannot hang the caller forever.
+
     Returns a deduped, length-capped list of candidate strings (may be empty).
-    Raises ValueError for an unknown mode. Client/connection errors propagate to
-    the caller.
+    Raises ValueError for an unknown mode and LLMTimeoutError if the request
+    exceeds ``timeout``. Other client/connection errors propagate to the caller.
     """
     request = _build_request(mode, context_data)
 
     client = instructor.from_openai(
-        OpenAI(base_url=f"{url}/v1", api_key="ollama"),
+        OpenAI(base_url=f"{url}/v1", api_key="ollama", timeout=timeout),
         mode=instructor.Mode.JSON,
     )
     prompt_generator = _TARGET_PROMPT if mode == "target" else _WORDLIST_PROMPT
@@ -117,7 +132,12 @@ def generate_candidates(
         )
     )
 
+    try:
         result = agent.run(GenerationInput(request=request))
+    except APITimeoutError as e:
+        raise LLMTimeoutError(
+            f"no response from {url} within {timeout:g} seconds"
+        ) from e
 
     seen: set[str] = set()
     candidates: list[str] = []

hate_crack/main.py

@@ -452,6 +452,7 @@ hcatDebugLogPath = os.path.expanduser(config_parser["hcatDebugLogPath"])
 ollamaUrl = "http://" + os.environ.get("OLLAMA_HOST", "localhost:11434")
 ollamaModel = config_parser.get("ollamaModel", "qwen2.5:32b")
 ollamaNumCtx = int(config_parser.get("ollamaNumCtx", 2048))
+ollamaTimeout = float(config_parser.get("ollamaTimeout", 300))
 
 omenTrainingList = config_parser.get("omenTrainingList", "rockyou.txt")
 omenMaxCandidates = int(config_parser.get("omenMaxCandidates", 1000000))
@@ -2072,8 +2073,20 @@ def hcatOllama(hcatHashType, hcatHashFile, mode, context_data):
     print(f"Generating password candidates via Ollama ({ollamaModel})...")
     try:
         candidates = llm.generate_candidates(
-            ollamaUrl, ollamaModel, ollamaNumCtx, mode, gen_context
+            ollamaUrl,
+            ollamaModel,
+            ollamaNumCtx,
+            mode,
+            gen_context,
+            timeout=ollamaTimeout,
         )
+    except llm.LLMTimeoutError:
+        print(f"Error: the Ollama request timed out after {ollamaTimeout:g} seconds.")
+        print(
+            f"The model ({ollamaModel}) may still be loading into VRAM. Retry, or "
+            'raise "ollamaTimeout" in config.json to wait longer.'
+        )
+        return
     except ValueError as e:
         # Defensive: mode is already validated above, but keep an explicit,
         # non-misleading message if generate_candidates ever rejects its input.

tests/test_hcat_ollama.py

@@ -179,6 +179,48 @@ def test_generation_error_reports_and_aborts(ollama_env, capsys):
     popen.assert_not_called()
 
 
+def test_timeout_error_reports_timeout_guidance(ollama_env, capsys):
+    with ollama_globals(ollama_env.tmp_path), \
+         mock.patch.object(hc_main, "ollamaTimeout", 300.0), \
+         mock.patch("hate_crack.main.llm.generate_candidates",
+                    side_effect=hc_main.llm.LLMTimeoutError("timed out")), \
+         mock.patch("subprocess.Popen") as popen:
+        hc_main.hcatOllama("0", ollama_env.hash_file, "target",
+                           {"company": "X", "industry": "Y", "location": "Z"})
+    captured = capsys.readouterr()
+    assert "timed out" in captured.out.lower()
+    assert "300" in captured.out
+    assert "ollamaTimeout" in captured.out
+    assert "Ensure Ollama is running" not in captured.out
+    popen.assert_not_called()
+    assert not os.path.isfile(f"{ollama_env.hash_file}.ollama_candidates")
+
+
+def test_value_error_reports_message_and_aborts(ollama_env, capsys):
+    """Covers the defensive `except ValueError` handler in hcatOllama."""
+    with ollama_globals(ollama_env.tmp_path), \
+         mock.patch("hate_crack.main.llm.generate_candidates",
+                    side_effect=ValueError("boom")), \
+         mock.patch("subprocess.Popen") as popen:
+        hc_main.hcatOllama("0", ollama_env.hash_file, "target",
+                           {"company": "X", "industry": "Y", "location": "Z"})
+    captured = capsys.readouterr()
+    assert "Error: boom" in captured.out
+    popen.assert_not_called()
+    assert not os.path.isfile(f"{ollama_env.hash_file}.ollama_candidates")
+
+
+def test_timeout_config_forwarded_to_generate_candidates(ollama_env):
+    with ollama_globals(ollama_env.tmp_path), \
+         mock.patch.object(hc_main, "ollamaTimeout", 77.0), \
+         mock.patch("hate_crack.main.llm.generate_candidates",
+                    return_value=["Password1"]) as gen, \
+         mock.patch("subprocess.Popen", return_value=_make_proc()):
+        hc_main.hcatOllama("0", ollama_env.hash_file, "target",
+                           {"company": "X", "industry": "Y", "location": "Z"})
+    assert gen.call_args.kwargs["timeout"] == 77.0
+
+
 def test_unknown_mode_prints_error(ollama_env, capsys):
     with ollama_globals(ollama_env.tmp_path), \
          mock.patch("hate_crack.main.llm.generate_candidates") as gen:

tests/test_llm.py

@@ -3,7 +3,9 @@
 import os
 from unittest import mock
 
+import httpx
 import instructor
+import openai
 import pytest
 
 os.environ["HATE_CRACK_SKIP_INIT"] = "1"
@@ -94,8 +96,52 @@ def test_num_ctx_forwarded_via_model_api_parameters():
     assert config.model_api_parameters["extra_body"]["options"]["num_ctx"] == 4096
 
 
-def test_unknown_mode_raises():
-    with pytest.raises(ValueError):
+def test_build_request_rejects_unknown_mode():
+    """Unit test of _build_request's mode validation only — no agent involved."""
+    with pytest.raises(ValueError, match="Unknown LLM generation mode: bogus"):
+        llm._build_request("bogus", {})
+
+
+def test_generate_candidates_rejects_unknown_mode_before_building_client():
+    """generate_candidates surfaces _build_request's ValueError to its caller."""
+    with pytest.raises(ValueError, match="Unknown LLM generation mode: bogus"):
         llm.generate_candidates(
             "http://localhost:11434", "qwen2.5:32b", 2048, "bogus", {},
         )
+
+
+def test_timeout_forwarded_to_openai_client():
+    p_instr, p_openai, p_agent, agent_cls, agent_instance = _patch_agent(["x"])
+    with p_instr, p_openai as openai_cls, p_agent:
+        llm.generate_candidates(
+            "http://localhost:11434", "qwen2.5:32b", 2048,
+            "target", {"company": "X", "industry": "Y", "location": "Z"},
+            timeout=42.5,
+        )
+    assert openai_cls.call_args.kwargs["timeout"] == 42.5
+
+
+def test_default_timeout_used_when_omitted():
+    p_instr, p_openai, p_agent, agent_cls, agent_instance = _patch_agent(["x"])
+    with p_instr, p_openai as openai_cls, p_agent:
+        llm.generate_candidates(
+            "http://localhost:11434", "qwen2.5:32b", 2048,
+            "target", {"company": "X", "industry": "Y", "location": "Z"},
+        )
+    assert llm.DEFAULT_TIMEOUT_SECONDS == 300.0
+    assert openai_cls.call_args.kwargs["timeout"] == llm.DEFAULT_TIMEOUT_SECONDS
+
+
+def test_api_timeout_reraised_as_domain_error():
+    """openai.APITimeoutError is translated into llm.LLMTimeoutError."""
+    p_instr, p_openai, p_agent, agent_cls, agent_instance = _patch_agent(["x"])
+    agent_instance.run.side_effect = openai.APITimeoutError(
+        request=httpx.Request("POST", "http://localhost:11434/v1/chat/completions")
+    )
+    with p_instr, p_openai, p_agent:
+        with pytest.raises(llm.LLMTimeoutError):
+            llm.generate_candidates(
+                "http://localhost:11434", "qwen2.5:32b", 2048,
+                "target", {"company": "X", "industry": "Y", "location": "Z"},
+                timeout=1.0,
+            )