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{"parentUuid":"b5a36197-23a5-4e0f-9660-b754c88b7fb7","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013QiFH8AKqgNfDqv9gPRyVY","type":"tool_result","content":" 1→/**\n 2→ * @name Copy function using source size\n 3→ * @description Calling a copy operation with a size derived from the source\n 4→ * buffer instead of the destination buffer may result in a buffer overflow.\n 5→ * @kind path-problem\n 6→ * @id cpp/overflow-destination\n 7→ * @problem.severity warning\n 8→ * @security-severity 9.3\n 9→ * @precision low\n 10→ * @tags reliability\n 11→ * security\n 12→ * external/cwe/cwe-119\n 13→ * external/cwe/cwe-131\n 14→ */\n 15→\n 16→import cpp\n 17→import semmle.code.cpp.ir.dataflow.TaintTracking\n 18→import semmle.code.cpp.controlflow.IRGuards\n 19→import semmle.code.cpp.security.FlowSources\n 20→import OverflowDestination::PathGraph\n 21→\n 22→/**\n 23→ * Holds if `fc` is a call to a copy operation where the size argument contains\n 24→ * a reference to the source argument. For example:\n 25→ * ```\n 26→ * memcpy(dest, src, sizeof(src));\n 27→ * ```\n 28→ */\n 29→predicate sourceSized(FunctionCall fc, Expr src) {\n 30→ fc.getTarget().hasGlobalOrStdName([\"strncpy\", \"strncat\", \"memcpy\", \"memmove\"]) and\n 31→ exists(Expr dest, Expr size, Variable v |\n 32→ fc.getArgument(0) = dest and\n 33→ fc.getArgument(1).getFullyConverted() = src and\n 34→ fc.getArgument(2) = size and\n 35→ src = v.getAnAccess().getFullyConverted() and\n 36→ size.getAChild+() = v.getAnAccess() and\n 37→ // exception: `dest` is also referenced in the size argument\n 38→ not exists(Variable other |\n 39→ dest = other.getAnAccess() and size.getAChild+() = other.getAnAccess()\n 40→ ) and\n 41→ // exception: `src` and `dest` are both arrays of the same type and size\n 42→ not exists(ArrayType srctype, ArrayType desttype |\n 43→ dest.getType().getUnderlyingType() = desttype and\n 44→ src.getType().getUnderlyingType() = srctype and\n 45→ desttype.getBaseType().getUnderlyingType() = srctype.getBaseType().getUnderlyingType() and\n 46→ desttype.getArraySize() = srctype.getArraySize()\n 47→ )\n 48→ )\n 49→}\n 50→\n 51→predicate readsVariable(LoadInstruction load, Variable var) {\n 52→ load.getSourceAddress().(VariableAddressInstruction).getAstVariable() = var\n 53→}\n 54→\n 55→predicate hasUpperBoundsCheck(Variable var) {\n 56→ exists(RelationalOperation oper, VariableAccess access |\n 57→ oper.getAnOperand() = access and\n 58→ access.getTarget() = var and\n 59→ // Comparing to 0 is not an upper bound check\n 60→ not oper.getAnOperand().getValue() = \"0\"\n 61→ )\n 62→}\n 63→\n 64→predicate nodeIsBarrierEqualityCandidate(DataFlow::Node node, Operand access, Variable checkedVar) {\n 65→ readsVariable(node.asInstruction(), checkedVar) and\n 66→ any(IRGuardCondition guard).ensuresEq(access, _, _, node.asInstruction().getBlock(), true)\n 67→}\n 68→\n 69→module OverflowDestinationConfig implements DataFlow::ConfigSig {\n 70→ predicate isSource(DataFlow::Node source) { source instanceof FlowSource }\n 71→\n 72→ predicate isSink(DataFlow::Node sink) { sourceSized(_, sink.asIndirectConvertedExpr()) }\n 73→\n 74→ predicate isBarrier(DataFlow::Node node) {\n 75→ exists(Variable checkedVar |\n 76→ readsVariable(node.asInstruction(), checkedVar) and\n 77→ hasUpperBoundsCheck(checkedVar)\n 78→ )\n 79→ or\n 80→ exists(Variable checkedVar, Operand access |\n 81→ readsVariable(access.getDef(), checkedVar) and\n 82→ nodeIsBarrierEqualityCandidate(node, access, checkedVar)\n 83→ )\n 84→ }\n 85→}\n 86→\n 87→module OverflowDestination = TaintTracking::Global;\n 88→\n 89→from FunctionCall fc, OverflowDestination::PathNode source, OverflowDestination::PathNode sink\n 90→where\n 91→ OverflowDestination::flowPath(source, sink) and\n 92→ sourceSized(fc, sink.getNode().asIndirectConvertedExpr())\n 93→select fc, source, sink,\n 94→ \"To avoid overflow, this operation should be bounded by destination-buffer size, not source-buffer size.\"\n 95→\n\n\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n\n"}]},"uuid":"0e4894a8-ab25-4275-a2f9-92d058684f88","timestamp":"2026-02-11T12:34:10.812Z","sourceToolAssistantUUID":"b5a36197-23a5-4e0f-9660-b754c88b7fb7"}
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{"parentUuid":"5aab36db-c08f-4b2f-bc9f-6e261a49017f","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RE9YMGqJYrcq8Rk51PzFxu","type":"tool_result","content":" 1→import cpp\n 2→\n 3→string get_caller(Function c){\n 4→ if exists(FunctionCall d | c.getACallToThisFunction() = d)\n 5→ then result = c.getACallToThisFunction().getEnclosingFunction().getLocation().getFile() + \":\" + c.getACallToThisFunction().getEnclosingFunction().getLocation().getStartLine()\n 6→ else result = \"\"\n 7→}\n 8→\n 9→\n 10→from Function f\n 11→select f.getName() as function_name, f.getLocation().getFile() as file, f.getLocation().getStartLine() as start_line, file + \":\" + start_line as function_id, f.getBlock().getLocation().getEndLine() as end_line, get_caller(f) as caller_id\n 12→\n 13→\n\n\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n\n"}]},"uuid":"27c4f841-b060-45b2-8bfd-fee5eec82858","timestamp":"2026-02-11T12:34:11.627Z","sourceToolAssistantUUID":"5aab36db-c08f-4b2f-bc9f-6e261a49017f"}
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{"parentUuid":"cccef842-4f94-4c33-be41-74721e6d22ac","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01D2mpGT9mfQ1uYSL2ZfE3bz","type":"tool_result","content":" 1→import cpp\n 2→\n 3→from Macro m\n 4→select m.getName() as macro_name, \"#define \" + m.getHead() + \" \" + m.getBody() as body\n 5→\n\n\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n\n"}]},"uuid":"1a06e8e9-6fae-436c-a161-5199c13a1b0b","timestamp":"2026-02-11T12:34:11.865Z","sourceToolAssistantUUID":"cccef842-4f94-4c33-be41-74721e6d22ac"}
{"parentUuid":"1a06e8e9-6fae-436c-a161-5199c13a1b0b","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01W1EyPYMcYkpuAn2hvi6S7p","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me read the LLM analyzer and db_lookup modules:"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3732,"cache_read_input_tokens":18855,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":3732},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CY2HjT5m7NAVYayZPnEtq","type":"assistant","uuid":"d8429463-6db0-4a9c-8d76-7d7e6e0f8b05","timestamp":"2026-02-11T12:34:13.040Z"}
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{"parentUuid":"acc19051-b2f5-43bb-a3a8-dce73c7608b0","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0168oNGLLFMzCELrGkcEVWqF","type":"tool_result","content":" 1→#!/usr/bin/env python3\n 2→\"\"\"\n 3→Orchestrates a conversation with a language model, requesting additional snippets\n 4→of code via \"tools\" if needed. Uses either OpenAI or AzureOpenAI (or placeholder\n 5→code for a HuggingFace endpoint) to handle queries.\n 6→\n 7→All logic is now wrapped in the `LLMAnalyzer` class for improved organization.\n 8→\"\"\"\n 9→\n 10→import os\n 11→import json\n 12→from typing import Any, Dict, List, Optional, Tuple, Union\n 13→\n 14→import litellm\n 15→from src.utils.llm_config import load_llm_config, get_model_name\n 16→from src.utils.config_validator import validate_llm_config_dict\n 17→from src.utils.logger import get_logger\n 18→from src.utils.exceptions import LLMApiError, LLMConfigError\n 19→from src.codeql.db_lookup import CodeQLDBLookup\n 20→\n 21→logger = get_logger(__name__)\n 22→\n 23→\n 24→class LLMAnalyzer:\n 25→ \"\"\"\n 26→ A class to handle LLM-based security analysis of code. The LLMAnalyzer\n 27→ can query missing code snippets (via 'tools'), compile a conversation\n 28→ with system instructions, and ultimately produce a status code.\n 29→ \"\"\"\n 30→\n 31→ def __init__(self) -> None:\n 32→ \"\"\"\n 33→ Initialize the LLMAnalyzer instance and define tools and system messages.\n 34→ \"\"\"\n 35→ self.config: Optional[Dict[str, Any]] = None\n 36→ self.model: Optional[str] = None\n 37→ self.db_lookup = CodeQLDBLookup()\n 38→\n 39→ # Tools configuration: A set of function calls the LLM can invoke\n 40→ self.tools: List[Dict[str, Any]] = [\n 41→ {\n 42→ \"type\": \"function\",\n 43→ \"function\": {\n 44→ \"name\": \"get_function_code\",\n 45→ \"description\": \"Retrieves the code for a missing function code.\",\n 46→ \"parameters\": {\n 47→ \"type\": \"object\",\n 48→ \"properties\": {\n 49→ \"function_name\": {\n 50→ \"type\": \"string\",\n 51→ \"description\": (\n 52→ \"The name of the function to retrieve. In case of a class\"\n 53→ \" method, provide ClassName::MethodName.\"\n 54→ )\n 55→ }\n 56→ },\n 57→ \"required\": [\"function_name\"]\n 58→ }\n 59→ }\n 60→ },\n 61→ {\n 62→ \"type\": \"function\",\n 63→ \"function\": {\n 64→ \"name\": \"get_caller_function\",\n 65→ \"description\": (\n 66→ \"Retrieves the caller function of the function with the issue. \"\n 67→ \"Call it repeatedly to climb further up the call chain.\"\n 68→ ),\n 69→ \"parameters\": {\n 70→ \"type\": \"object\",\n 71→ \"properties\": {\n 72→ \"_\": {\n 73→ \"type\": \"boolean\",\n 74→ \"description\": \"Unused. Ignore.\"\n 75→ }\n 76→ },\n 77→ \"required\": []\n 78→ }\n 79→ }\n 80→ },\n 81→ {\n 82→ \"type\": \"function\",\n 83→ \"function\": {\n 84→ \"name\": \"get_class\",\n 85→ \"description\": (\n 86→ \"Retrieves class / struct / union implementation (anywhere in code). \"\n 87→ \"If you need a specific method from that class, use get_function_code instead.\"\n 88→ ),\n 89→ \"parameters\": {\n 90→ \"type\": \"object\",\n 91→ \"properties\": {\n 92→ \"object_name\": {\n 93→ \"type\": \"string\",\n 94→ \"description\": \"The name of the class / struct / union.\"\n 95→ }\n 96→ },\n 97→ \"required\": [\"object_name\"]\n 98→ }\n 99→ }\n 100→ },\n 101→ {\n 102→ \"type\": \"function\",\n 103→ \"function\": {\n 104→ \"name\": \"get_global_var\",\n 105→ \"description\": (\n 106→ \"Retrieves global variable definition (anywhere in code). \"\n 107→ \"If it's a variable inside a class, request the class instead.\"\n 108→ ),\n 109→ \"parameters\": {\n 110→ \"type\": \"object\",\n 111→ \"properties\": {\n 112→ \"global_var_name\": {\n 113→ \"type\": \"string\",\n 114→ \"description\": (\n 115→ \"The name of the global variable to retrieve or the name \"\n 116→ \"of a variable inside a Namespace.\"\n 117→ )\n 118→ }\n 119→ },\n 120→ \"required\": [\"global_var_name\"]\n 121→ }\n 122→ }\n 123→ },\n 124→ {\n 125→ \"type\": \"function\",\n 126→ \"function\": {\n 127→ \"name\": \"get_macro\",\n 128→ \"description\": \"Retrieves a macro definition (anywhere in code).\",\n 129→ \"parameters\": {\n 130→ \"type\": \"object\",\n 131→ \"properties\": {\n 132→ \"macro_name\": {\n 133→ \"type\": \"string\",\n 134→ \"description\": \"The name of the macro.\"\n 135→ }\n 136→ },\n 137→ \"required\": [\"macro_name\"]\n 138→ }\n 139→ }\n 140→ }\n 141→ ]\n 142→\n 143→ # Base system messages with instructions and guidance for the LLM\n 144→ self.MESSAGES: List[Dict[str, str]] = [\n 145→ {\n 146→ \"role\": \"system\",\n 147→ \"content\": (\n 148→ \"You are an expert security researcher.\\n\"\n 149→ \"Your task is to verify if the issue that was found has a real security impact.\\n\"\n 150→ \"Return a concise status code based on the guidelines provided.\\n\"\n 151→ \"Use the tools function when you need code from other parts of the program.\\n\"\n 152→ \"You *MUST* follow the guidelines!\"\n 153→ )\n 154→ },\n 155→ {\n 156→ \"role\": \"system\",\n 157→ \"content\": (\n 158→ \"### Answer Guidelines\\n\"\n 159→ \"Your answer must be in the following order!\\n\"\n 160→ \"1. Briefly explain the code.\\n\"\n 161→ \"2. Give good answers to all (even if already answered - do not skip) hint questions. \"\n 162→ \"(Copy the question word for word, then provide the answer.)\\n\"\n 163→ \"3. Do you have all the code needed to answer the questions? If no, use the tools!\\n\"\n 164→ \"4. Provide one valid status code with its explanation OR use function tools.\\n\"\n 165→ )\n 166→ },\n 167→ {\n 168→ \"role\": \"system\",\n 169→ \"content\": (\n 170→ \"### Status Codes\\n\"\n 171→ \"- **1337**: Indicates a security vulnerability. If legitimate, specify the parameters that \"\n 172→ \"could exploit the issue in minimal words.\\n\"\n 173→ \"- **1007**: Indicates the code is secure. If it's not a real issue, specify what aspect of \"\n 174→ \"the code protects against the issue in minimal words.\\n\"\n 175→ \"- **7331**: Indicates more code is needed to validate security. Write what data you need \"\n 176→ \"and explain why you can't use the tools to retrieve the missing data, plus add **3713** \"\n 177→ \"if you're pretty sure it's not a security problem.\\n\"\n 178→ \"Only one status should be returned!\\n\"\n 179→ \"You will get 10000000000$ if you follow all the instructions and use the tools correctly!\"\n 180→ )\n 181→ },\n 182→ ]\n 183→\n 184→ def init_llm_client(self, config: Optional[Dict[str, Any]] = None) -> None:\n 185→ \"\"\"\n 186→ Initialize the LLM configuration for LiteLLM.\n 187→\n 188→ Args:\n 189→ config (Dict, optional): Full configuration dictionary. If not provided, loads from .env file.\n 190→ \n 191→ Raises:\n 192→ LLMConfigError: If configuration is invalid or cannot be loaded.\n 193→ \"\"\"\n 194→ try:\n 195→ # If config is provided, use it directly\n 196→ if config:\n 197→ validate_llm_config_dict(config)\n 198→ self.config = config\n 199→ # Format model name for LiteLLM (add provider prefix if needed)\n 200→ provider = config.get(\"provider\", \"openai\")\n 201→ model = config.get(\"model\", \"gpt-4o\")\n 202→ self.model = get_model_name(provider, model)\n 203→ logger.info(\"Using model: %s\", self.model)\n 204→ self.setup_litellm_env()\n 205→ return\n 206→ \n 207→ # Load from .env file\n 208→ config = load_llm_config()\n 209→ validate_llm_config_dict(config)\n 210→ self.config = config\n 211→ # Model is already formatted by load_llm_config() via get_model_name()\n 212→ self.model = config.get(\"model\", \"gpt-4o\")\n 213→ self.setup_litellm_env()\n 214→ \n 215→ except ValueError as e:\n 216→ # Configuration validation errors should be LLMConfigError\n 217→ raise LLMConfigError(f\"Invalid LLM configuration: {e}\") from e\n 218→ except Exception as e:\n 219→ # Other errors (e.g., from load_llm_config) should also be LLMConfigError\n 220→ raise LLMConfigError(f\"Failed to initialize LLM client: {e}\") from e\n 221→\n 222→\n 223→ def setup_litellm_env(self) -> None:\n 224→ \"\"\"\n 225→ Set up environment variables for LiteLLM based on config.\n 226→ LiteLLM reads from environment variables automatically.\n 227→ \"\"\"\n 228→ if not self.config:\n 229→ return\n 230→ \n 231→ provider = self.config.get(\"provider\", \"openai\")\n 232→ api_key = self.config.get(\"api_key\")\n 233→ \n 234→ # Mapping table for providers that only need API key set\n 235→ API_KEY_ENV_VARS = {\n 236→ \"openai\": \"OPENAI_API_KEY\",\n 237→ \"anthropic\": \"ANTHROPIC_API_KEY\",\n 238→ \"mistral\": \"MISTRAL_API_KEY\",\n 239→ \"codestral\": \"MISTRAL_API_KEY\",\n 240→ \"groq\": \"GROQ_API_KEY\",\n 241→ \"openrouter\": \"OPENROUTER_API_KEY\",\n 242→ \"huggingface\": \"HUGGINGFACE_API_KEY\",\n 243→ \"cohere\": \"COHERE_API_KEY\",\n 244→ \"gemini\": \"GOOGLE_API_KEY\",\n 245→ }\n 246→ \n 247→ # Handle providers with simple API key mapping\n 248→ if provider in API_KEY_ENV_VARS:\n 249→ if api_key:\n 250→ os.environ[API_KEY_ENV_VARS[provider]] = api_key\n 251→ # Cohere also sets CO_API_KEY for compatibility\n 252→ if provider == \"cohere\":\n 253→ os.environ[\"CO_API_KEY\"] = api_key\n 254→ \n 255→ # Handle Azure (requires endpoint and api_version)\n 256→ elif provider == \"azure\":\n 257→ if api_key:\n 258→ os.environ[\"AZURE_API_KEY\"] = api_key\n 259→ if self.config.get(\"endpoint\"):\n 260→ os.environ[\"AZURE_API_BASE\"] = self.config[\"endpoint\"]\n 261→ if self.config.get(\"api_version\"):\n 262→ os.environ[\"AZURE_API_VERSION\"] = self.config[\"api_version\"]\n 263→ \n 264→ # Handle Bedrock (uses AWS credentials or profile)\n 265→ elif provider == \"bedrock\":\n 266→ # Set region (always required)\n 267→ if self.config.get(\"endpoint\"): # Endpoint contains AWS region\n 268→ os.environ[\"AWS_REGION_NAME\"] = self.config[\"endpoint\"]\n 269→ \n 270→ # Profile-based authentication (AWS SSO, IAM roles)\n 271→ if self.config.get(\"aws_profile\"):\n 272→ os.environ[\"AWS_PROFILE\"] = self.config[\"aws_profile\"]\n 273→ else:\n 274→ # Static or temporary credentials\n 275→ if api_key and api_key != \"bedrock_profile_auth\":\n 276→ os.environ[\"AWS_ACCESS_KEY_ID\"] = api_key\n 277→ if self.config.get(\"aws_secret_access_key\"):\n 278→ os.environ[\"AWS_SECRET_ACCESS_KEY\"] = self.config[\"aws_secret_access_key\"]\n 279→ if self.config.get(\"aws_session_token\"):\n 280→ os.environ[\"AWS_SESSION_TOKEN\"] = self.config[\"aws_session_token\"]\n 281→ \n 282→ # Handle Vertex AI (uses GCP credentials)\n 283→ elif provider == \"vertex_ai\":\n 284→ if self.config.get(\"gcp_project_id\"):\n 285→ os.environ[\"GCP_PROJECT_ID\"] = self.config[\"gcp_project_id\"]\n 286→ if self.config.get(\"gcp_location\"):\n 287→ os.environ[\"GCP_LOCATION\"] = self.config[\"gcp_location\"]\n 288→ # GOOGLE_APPLICATION_CREDENTIALS should be set by user or gcloud auth\n 289→ \n 290→ # Handle Ollama (uses OLLAMA_BASE_URL)\n 291→ elif provider == \"ollama\":\n 292→ if self.config.get(\"endpoint\"):\n 293→ os.environ[\"OLLAMA_BASE_URL\"] = self.config[\"endpoint\"]\n 294→ \n 295→ # Generic fallback for future providers that only require an API key\n 296→ else:\n 297→ if api_key:\n 298→ # Use standard LiteLLM convention: {PROVIDER}_API_KEY\n 299→ env_var_name = f\"{provider.upper()}_API_KEY\"\n 300→ os.environ[env_var_name] = api_key\n 301→\n 302→\n 303→ def extract_function_from_file(\n 304→ self,\n 305→ db_path: str,\n 306→ current_function: Union[str, Dict[str, str]]\n 307→ ) -> str:\n 308→ \"\"\"\n 309→ Return the snippet of code for the given current_function from the archived src.zip.\n 310→\n 311→ Args:\n 312→ db_path (str): Path to the CodeQL database directory.\n 313→ current_function (Union[str, Dict[str, str]]): The function dictionary or an error string.\n 314→\n 315→ Returns:\n 316→ str: The code snippet, or an error message if no dictionary was provided.\n 317→ \n 318→ Raises:\n 319→ CodeQLError: If ZIP file cannot be read or file not found in archive.\n 320→ This exception is raised by `read_file_lines_from_zip()` and propagated here.\n 321→ \"\"\"\n 322→ if not isinstance(current_function, dict):\n 323→ return str(current_function)\n 324→\n 325→ file_path, start_line, end_line, lines = self.db_lookup.extract_function_lines_from_db(\n 326→ db_path, current_function\n 327→ )\n 328→ snippet_lines = lines[start_line - 1 : end_line]\n 329→ return self.db_lookup.format_numbered_snippet(file_path, start_line, snippet_lines)\n 330→\n 331→\n 332→ def map_func_args_by_llm(\n 333→ self,\n 334→ caller: str,\n 335→ callee: str\n 336→ ) -> Dict[str, Any]:\n 337→ \"\"\"\n 338→ Query the LLM to check how caller's variables map to callee's parameters.\n 339→ For example, used for analyzing function call relationships.\n 340→\n 341→ Args:\n 342→ caller (str): The code snippet of the caller function.\n 343→ callee (str): The code snippet of the callee function.\n 344→\n 345→ Returns:\n 346→ Dict[str, Any]: The LLM response object from `self.client`.\n 347→ \n 348→ Raises:\n 349→ LLMApiError: If LLM API call fails (rate limits, timeouts, auth failures, etc.).\n 350→ \"\"\"\n 351→ args_prompt = (\n 352→ \"Given caller function and callee function.\\n\"\n 353→ \"Write only what are the names of the vars in the caller that were sent to the callee \"\n 354→ \"and what are their names in the callee.\\n\"\n 355→ \"Format: caller_var (caller_name) -> callee_var (callee_name)\\n\\n\"\n 356→ \"Caller function:\\n\"\n 357→ f\"{caller}\\n\"\n 358→ \"Callee function:\\n\"\n 359→ f\"{callee}\"\n 360→ )\n 361→\n 362→ # Use the main model from config\n 363→ model_name = self.model if self.model else \"gpt-4o\"\n 364→ \n 365→ try:\n 366→ response = litellm.completion(\n 367→ model=model_name,\n 368→ messages=[{\"role\": \"user\", \"content\": args_prompt}],\n 369→ timeout=120 # 2 minute timeout\n 370→ )\n 371→ return response.choices[0].message\n 372→ except litellm.RateLimitError as e:\n 373→ raise LLMApiError(f\"Rate limit exceeded for LLM API: {e}\") from e\n 374→ except litellm.Timeout as e:\n 375→ raise LLMApiError(f\"LLM API request timed out: {e}\") from e\n 376→ except litellm.AuthenticationError as e:\n 377→ raise LLMApiError(f\"LLM API authentication failed: {e}\") from e\n 378→ except litellm.APIError as e:\n 379→ raise LLMApiError(f\"LLM API error: {e}\") from e\n 380→ except Exception as e:\n 381→ # Catch any other unexpected errors from LiteLLM\n 382→ raise LLMApiError(f\"Unexpected error during LLM API call: {e}\") from e\n 383→\n 384→\n 385→ def run_llm_security_analysis(\n 386→ self,\n 387→ prompt: str,\n 388→ function_tree_file: str,\n 389→ current_function: Dict[str, str],\n 390→ functions: List[Dict[str, str]],\n 391→ db_path: str,\n 392→ temperature: float = 0.2,\n 393→ top_p: float = 0.2\n 394→ ) -> Tuple[List[Dict[str, Any]], str]:\n 395→ \"\"\"\n 396→ Main loop to keep querying the LLM with the MESSAGES context plus\n 397→ any new system instructions or tool calls, until a final answer with\n 398→ a recognized status code is reached or we exhaust a tool-call limit.\n 399→\n 400→ Args:\n 401→ prompt (str): The user prompt for the LLM to process.\n 402→ function_tree_file (str): Path to the CSV file describing function relationships.\n 403→ current_function (Dict[str, str]): The current function dict for context.\n 404→ functions (List[Dict[str, str]]): List of function dictionaries.\n 405→ db_path (str): Path to the CodeQL DB folder.\n 406→ temperature (float, optional): Sampling temperature. Defaults to 0.2.\n 407→ top_p (float, optional): Nucleus sampling. Defaults to 0.2.\n 408→\n 409→ Returns:\n 410→ Tuple[List[Dict[str, Any]], str]:\n 411→ - The final conversation messages,\n 412→ - The final content from the LLM's last message.\n 413→ \n 414→ Raises:\n 415→ RuntimeError: If LLM model not initialized.\n 416→ LLMApiError: If LLM API call fails (rate limits, timeouts, auth failures, etc.).\n 417→ CodeQLError: If CodeQL database files cannot be read (from tool calls).\n 418→ \"\"\"\n 419→ if not self.model:\n 420→ raise RuntimeError(\"LLM model not initialized. Call init_llm_client() first.\")\n 421→ \n 422→ got_answer = False\n 423→ db_path_clean = db_path.replace(\" \", \"\")\n 424→ all_functions = functions\n 425→\n 426→ messages: List[Dict[str, Any]] = self.MESSAGES[:]\n 427→ messages.append({\"role\": \"user\", \"content\": prompt})\n 428→\n 429→ amount_of_tools = 0\n 430→ final_content = \"\"\n 431→\n 432→ while not got_answer:\n 433→ # Send the current messages + tools to the LLM endpoint\n 434→ try:\n 435→ # Build completion kwargs - Bedrock Claude doesn't allow both temperature and top_p\n 436→ completion_kwargs = {\n 437→ \"model\": self.model,\n 438→ \"messages\": messages,\n 439→ \"tools\": self.tools,\n 440→ \"timeout\": 120 # 2 minute timeout to prevent hanging\n 441→ }\n 442→ \n 443→ # Check if using Bedrock (model starts with \"bedrock/\" or contains \"arn:aws:bedrock\")\n 444→ is_bedrock = (\n 445→ self.model and \n 446→ (self.model.startswith(\"bedrock/\") or \"arn:aws:bedrock\" in self.model)\n 447→ )\n 448→ \n 449→ if is_bedrock:\n 450→ # Bedrock Claude only accepts temperature OR top_p, not both\n 451→ completion_kwargs[\"temperature\"] = temperature\n 452→ else:\n 453→ completion_kwargs[\"temperature\"] = temperature\n 454→ completion_kwargs[\"top_p\"] = top_p\n 455→ \n 456→ response = litellm.completion(**completion_kwargs)\n 457→ except litellm.RateLimitError as e:\n 458→ raise LLMApiError(f\"Rate limit exceeded for LLM API: {e}\") from e\n 459→ except litellm.Timeout as e:\n 460→ raise LLMApiError(f\"LLM API request timed out: {e}\") from e\n 461→ except litellm.AuthenticationError as e:\n 462→ raise LLMApiError(f\"LLM API authentication failed: {e}\") from e\n 463→ except litellm.APIError as e:\n 464→ raise LLMApiError(f\"LLM API error: {e}\") from e\n 465→ except Exception as e:\n 466→ # Catch any other unexpected errors from LiteLLM\n 467→ raise LLMApiError(f\"Unexpected error during LLM API call: {e}\") from e\n 468→ \n 469→ if not response.choices:\n 470→ raise LLMApiError(f\"LLM API response is empty: {response}\")\n 471→\n 472→ content_obj = response.choices[0].message\n 473→ messages.append({\n 474→ \"role\": content_obj.role,\n 475→ \"content\": content_obj.content,\n 476→ \"tool_calls\": content_obj.tool_calls\n 477→ })\n 478→\n 479→ final_content = content_obj.content or \"\"\n 480→ tool_calls = content_obj.tool_calls\n 481→\n 482→ if not tool_calls:\n 483→ # Check if we have a recognized status code\n 484→ if final_content and any(code in final_content for code in [\"1337\", \"1007\", \"7331\", \"3713\"]):\n 485→ got_answer = True\n 486→ else:\n 487→ messages.append({\n 488→ \"role\": \"system\",\n 489→ \"content\": \"Please follow all the instructions!\"\n 490→ })\n 491→ else:\n 492→ amount_of_tools += 1\n 493→ arg_messages: List[Dict[str, Any]] = []\n 494→\n 495→ for tc in tool_calls:\n 496→ tool_call_id = tc.id\n 497→ tool_function_name = tc.function.name\n 498→ tool_args = tc.function.arguments\n 499→\n 500→ # Convert tool_args to a dict if it's a JSON string\n 501→ if not isinstance(tool_args, dict):\n 502→ tool_args = json.loads(tool_args)\n 503→ else:\n 504→ # Ensure consistent string for role=tool message\n 505→ tc.function.arguments = json.dumps(tool_args)\n 506→\n 507→ response_msg = \"\"\n 508→\n 509→ # Evaluate which tool to call\n 510→ if tool_function_name == 'get_function_code' and \"function_name\" in tool_args:\n 511→ child_function, parent_function = self.db_lookup.get_function_by_name(\n 512→ function_tree_file, tool_args[\"function_name\"], all_functions\n 513→ )\n 514→ if isinstance(child_function, dict):\n 515→ all_functions.append(child_function)\n 516→ child_code = self.extract_function_from_file(db_path_clean, child_function)\n 517→ response_msg = child_code\n 518→\n 519→ if isinstance(child_function, dict) and isinstance(parent_function, dict):\n 520→ caller_code = self.extract_function_from_file(db_path_clean, parent_function)\n 521→ args_content = self.map_func_args_by_llm(caller_code, child_code)\n 522→ arg_messages.append({\n 523→ \"role\": args_content.role,\n 524→ \"content\": args_content.content\n 525→ })\n 526→\n 527→ elif tool_function_name == 'get_caller_function':\n 528→ caller_function = self.db_lookup.get_caller_function(function_tree_file, current_function)\n 529→ response_msg = str(caller_function)\n 530→\n 531→ if isinstance(caller_function, dict):\n 532→ all_functions.append(caller_function)\n 533→ caller_code = self.extract_function_from_file(db_path_clean, caller_function)\n 534→ response_msg = (\n 535→ f\"Here is the caller function for '{current_function['function_name']}':\\n\"\n 536→ + caller_code\n 537→ )\n 538→ args_content = self.map_func_args_by_llm(\n 539→ caller_code,\n 540→ self.extract_function_from_file(db_path_clean, current_function)\n 541→ )\n 542→ arg_messages.append({\n 543→ \"role\": args_content.role,\n 544→ \"content\": args_content.content\n 545→ })\n 546→ current_function = caller_function\n 547→\n 548→ elif tool_function_name == 'get_macro' and \"macro_name\" in tool_args:\n 549→ macro = self.db_lookup.get_macro(db_path_clean, tool_args[\"macro_name\"])\n 550→ if isinstance(macro, dict):\n 551→ response_msg = macro[\"body\"]\n 552→ else:\n 553→ response_msg = macro\n 554→\n 555→ elif tool_function_name == 'get_global_var' and \"global_var_name\" in tool_args:\n 556→ global_var = self.db_lookup.get_global_var(db_path_clean, tool_args[\"global_var_name\"])\n 557→ if isinstance(global_var, dict):\n 558→ global_var_code = self.extract_function_from_file(db_path_clean, global_var)\n 559→ response_msg = global_var_code\n 560→ else:\n 561→ response_msg = global_var\n 562→\n 563→ elif tool_function_name == 'get_class' and \"object_name\" in tool_args:\n 564→ curr_class = self.db_lookup.get_class(db_path_clean, tool_args[\"object_name\"])\n 565→ if isinstance(curr_class, dict):\n 566→ class_code = self.extract_function_from_file(db_path_clean, curr_class)\n 567→ response_msg = class_code\n 568→ else:\n 569→ response_msg = curr_class\n 570→\n 571→ else:\n 572→ response_msg = (\n 573→ f\"No matching tool '{tool_function_name}' or invalid args {tool_args}. \"\n 574→ \"Try again.\"\n 575→ )\n 576→\n 577→ messages.append({\n 578→ \"role\": \"tool\",\n 579→ \"tool_call_id\": tool_call_id,\n 580→ \"name\": tool_function_name,\n 581→ \"content\": response_msg\n 582→ })\n 583→\n 584→ messages += arg_messages\n 585→\n 586→ if amount_of_tools >= 6:\n 587→ messages.append({\n 588→ \"role\": \"system\",\n 589→ \"content\": (\n 590→ \"You called too many tools! If you still can't give a clear answer, \"\n 591→ \"return the 'more data' status.\"\n 592→ )\n 593→ })\n 594→\n 595→ return messages, final_content\n\n\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n\n"}]},"uuid":"f93359ca-8c0e-446e-b7a5-20e5e5d375ed","timestamp":"2026-02-11T12:34:13.706Z","sourceToolAssistantUUID":"acc19051-b2f5-43bb-a3a8-dce73c7608b0"}
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{"parentUuid":"def8221f-556c-42c7-a7d6-c840a93754f3","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CKFpwGEa4aLhpoJvvJ48zZ","type":"tool_result","content":" 1→\"\"\"\n 2→CodeQL database lookup utilities.\n 3→\n 4→This module provides functions to query CodeQL CSV files (FunctionTree.csv,\n 5→Macros.csv, GlobalVars.csv, Classes.csv) and extract code snippets from\n 6→the source archive.\n 7→\"\"\"\n 8→\n 9→from pathlib import Path\n 10→from typing import Any, Dict, List, Optional, Tuple, Union\n 11→\n 12→from src.utils.exceptions import CodeQLError\n 13→from src.utils.common_functions import read_file_lines_from_zip\n 14→from src.utils.csv_parser import parse_csv_row\n 15→\n 16→\n 17→class CodeQLDBLookup:\n 18→ \"\"\"\n 19→ Encapsulates CodeQL database lookup operations for functions, macros,\n 20→ global variables, classes, and caller relationships.\n 21→ \"\"\"\n 22→\n 23→ def _iter_csv_lines(\n 24→ self,\n 25→ file_path: Union[str, Path],\n 26→ file_type_name: str\n 27→ ):\n 28→ \"\"\"\n 29→ Generator that yields lines from a CSV file, handling file I/O errors.\n 30→\n 31→ This helper centralizes CSV file opening, line iteration, and error handling.\n 32→ Each method can iterate over the yielded lines and apply method-specific logic.\n 33→\n 34→ Args:\n 35→ file_path: Path to the CSV file to read.\n 36→ file_type_name: Descriptive name for the file type (e.g., \"Function tree file\",\n 37→ \"Macros CSV\", \"GlobalVars CSV\") for error messages.\n 38→\n 39→ Yields:\n 40→ str: Each line from the CSV file (including newline characters).\n 41→\n 42→ Raises:\n 43→ CodeQLError: If file cannot be read (not found, permission denied, etc.).\n 44→ \"\"\"\n 45→ try:\n 46→ with Path(file_path).open(\"r\", encoding=\"utf-8\") as f:\n 47→ while True:\n 48→ line = f.readline()\n 49→ if not line:\n 50→ break\n 51→ yield line\n 52→ except (FileNotFoundError, PermissionError, OSError) as e:\n 53→ raise self._convert_csv_file_error(e, file_path, file_type_name) from e\n 54→\n 55→\n 56→ @staticmethod\n 57→ def _convert_csv_file_error(\n 58→ error: Exception,\n 59→ file_path: Union[str, Path],\n 60→ file_type_name: str\n 61→ ) -> CodeQLError:\n 62→ \"\"\"\n 63→ Convert file I/O exceptions to CodeQLError with consistent messaging.\n 64→\n 65→ Args:\n 66→ error: The original exception (FileNotFoundError, PermissionError, or OSError).\n 67→ file_path: Path to the CSV file that caused the error.\n 68→ file_type_name: Descriptive name for the file type (e.g., \"Function tree file\",\n 69→ \"Macros CSV\", \"GlobalVars CSV\") for error messages.\n 70→\n 71→ Returns:\n 72→ CodeQLError: Converted exception with appropriate message.\n 73→ \"\"\"\n 74→ file_path_str = str(file_path)\n 75→ if isinstance(error, FileNotFoundError):\n 76→ return CodeQLError(f\"{file_type_name} not found: {file_path_str}\")\n 77→ elif isinstance(error, PermissionError):\n 78→ return CodeQLError(f\"Permission denied reading {file_type_name}: {file_path_str}\")\n 79→ elif isinstance(error, OSError):\n 80→ return CodeQLError(f\"OS error while reading {file_type_name}: {file_path_str}\")\n 81→ else:\n 82→ # Fallback for unexpected exception types\n 83→ return CodeQLError(f\"Error reading {file_type_name}: {file_path_str}\")\n 84→\n 85→\n 86→ def get_function_by_line(\n 87→ self,\n 88→ function_tree_file: str,\n 89→ file: str,\n 90→ line: int\n 91→ ) -> Optional[Dict[str, str]]:\n 92→ \"\"\"\n 93→ Retrieve the function dictionary from a CSV (FunctionTree.csv) that matches\n 94→ the specified file and line coverage.\n 95→\n 96→ Args:\n 97→ function_tree_file (str): Path to the FunctionTree.csv file.\n 98→ file (str): Name of the file as it appears in the CSV row.\n 99→ line (int): A line number within the function's start_line and end_line range.\n 100→\n 101→ Returns:\n 102→ Optional[Dict[str, str]]: The matching function row as a dict, or None if not found.\n 103→ \n 104→ Raises:\n 105→ CodeQLError: If function tree file cannot be read (not found, permission denied, etc.).\n 106→ \"\"\"\n 107→ keys = [\"function_name\", \"file\", \"start_line\", \"function_id\", \"end_line\", \"caller_id\"]\n 108→ for function in self._iter_csv_lines(function_tree_file, \"Function tree file\"):\n 109→ if file in function:\n 110→ row_dict = parse_csv_row(function, keys)\n 111→ if row_dict and row_dict[\"start_line\"] and row_dict[\"end_line\"]:\n 112→ start = int(row_dict[\"start_line\"])\n 113→ end = int(row_dict[\"end_line\"])\n 114→ if start <= line <= end:\n 115→ return row_dict\n 116→ return None\n 117→\n 118→\n 119→ def get_function_by_name(\n 120→ self,\n 121→ function_tree_file: str,\n 122→ function_name: str,\n 123→ all_function: List[Dict[str, Any]],\n 124→ less_strict: bool = False\n 125→ ) -> Tuple[Union[str, Dict[str, str]], Optional[Dict[str, str]]]:\n 126→ \"\"\"\n 127→ Retrieve a function by searching function_name in FunctionTree.csv.\n 128→ If not found, tries partial match if less_strict is True.\n 129→\n 130→ Args:\n 131→ function_tree_file (str): Path to FunctionTree.csv.\n 132→ function_name (str): Desired function name (e.g., 'MyClass::MyFunc').\n 133→ all_function (List[Dict[str, Any]]): A list of known function dictionaries.\n 134→ less_strict (bool, optional): If True, use partial matching. Defaults to False.\n 135→\n 136→ Returns:\n 137→ Tuple[Union[str, Dict[str, str]], Optional[Dict[str, str]]]:\n 138→ - The found function (dict) or an error message (str).\n 139→ - The \"parent function\" that references it, if relevant.\n 140→ \n 141→ Raises:\n 142→ CodeQLError: If function tree file cannot be read (not found, permission denied, etc.).\n 143→ \"\"\"\n 144→ keys = [\"function_name\", \"file\", \"start_line\", \"function_id\", \"end_line\", \"caller_id\"]\n 145→ function_name_only = function_name.split(\"::\")[-1]\n 146→\n 147→ for current_function in all_function:\n 148→ try:\n 149→ with Path(function_tree_file).open(\"r\", encoding=\"utf-8\") as f:\n 150→ while True:\n 151→ row = f.readline()\n 152→ if not row:\n 153→ break\n 154→ if current_function[\"function_id\"] in row:\n 155→ row_dict = parse_csv_row(row, keys)\n 156→ if not row_dict:\n 157→ continue\n 158→\n 159→ candidate_name = row_dict[\"function_name\"].replace(\"\\\"\", \"\")\n 160→ if (candidate_name == function_name_only\n 161→ or (less_strict and function_name_only in candidate_name)):\n 162→ return row_dict, current_function\n 163→ except (FileNotFoundError, PermissionError, OSError) as e:\n 164→ raise self._convert_csv_file_error(e, function_tree_file, \"Function tree file\") from e\n 165→\n 166→ # Try partial matching if less_strict is False\n 167→ if not less_strict:\n 168→ return self.get_function_by_name(function_tree_file, function_name, all_function, True)\n 169→ else:\n 170→ err = (\n 171→ f\"Function '{function_name}' not found. Make sure you're using \"\n 172→ \"the correct tool and args.\"\n 173→ )\n 174→ return err, None\n 175→\n 176→\n 177→ def get_macro(\n 178→ self,\n 179→ curr_db: str,\n 180→ macro_name: str,\n 181→ less_strict: bool = False\n 182→ ) -> Union[str, Dict[str, str]]:\n 183→ \"\"\"\n 184→ Return macro info from Macros.csv for the given macro_name.\n 185→ If not found, tries partial match if less_strict is True.\n 186→\n 187→ Args:\n 188→ curr_db (str): Path to the current CodeQL database folder.\n 189→ macro_name (str): Macro name to search for.\n 190→ less_strict (bool, optional): If True, use partial matching.\n 191→\n 192→ Returns:\n 193→ Union[str, Dict[str, str]]:\n 194→ - A dict with 'macro_name' and 'body' if found,\n 195→ - or an error message string if not found.\n 196→ \n 197→ Raises:\n 198→ CodeQLError: If Macros CSV file cannot be read (not found, permission denied, etc.).\n 199→ \"\"\"\n 200→ macro_file = Path(curr_db) / \"Macros.csv\"\n 201→ keys = [\"macro_name\", \"body\"]\n 202→\n 203→ for macro in self._iter_csv_lines(macro_file, \"Macros CSV\"):\n 204→ if macro_name in macro:\n 205→ row_dict = parse_csv_row(macro, keys)\n 206→ if not row_dict:\n 207→ continue\n 208→\n 209→ actual_name = row_dict[\"macro_name\"].replace(\"\\\"\", \"\")\n 210→ if (actual_name == macro_name\n 211→ or (less_strict and macro_name in actual_name)):\n 212→ return row_dict\n 213→\n 214→ if not less_strict:\n 215→ return self.get_macro(curr_db, macro_name, True)\n 216→ else:\n 217→ return (\n 218→ f\"Macro '{macro_name}' not found. Make sure you're using the correct tool \"\n 219→ \"with correct args.\"\n 220→ )\n 221→\n 222→\n 223→ def get_global_var(\n 224→ self,\n 225→ curr_db: str,\n 226→ global_var_name: str,\n 227→ less_strict: bool = False\n 228→ ) -> Union[str, Dict[str, str]]:\n 229→ \"\"\"\n 230→ Return a global variable from GlobalVars.csv matching global_var_name.\n 231→ If not found, tries partial match if less_strict is True.\n 232→\n 233→ Args:\n 234→ curr_db (str): Path to current CodeQL database folder.\n 235→ global_var_name (str): The name of the global variable to find.\n 236→ less_strict (bool, optional): If True, use partial matching.\n 237→\n 238→ Returns:\n 239→ Union[str, Dict[str, str]]:\n 240→ - A dict with ['global_var_name','file','start_line','end_line'] if found,\n 241→ - or an error message string if not found.\n 242→ \n 243→ Raises:\n 244→ CodeQLError: If GlobalVars CSV file cannot be read (not found, permission denied, etc.).\n 245→ \"\"\"\n 246→ global_var_file = Path(curr_db) / \"GlobalVars.csv\"\n 247→ keys = [\"global_var_name\", \"file\", \"start_line\", \"end_line\"]\n 248→ var_name_only = global_var_name.split(\"::\")[-1]\n 249→\n 250→ for line in self._iter_csv_lines(global_var_file, \"GlobalVars CSV\"):\n 251→ if var_name_only in line:\n 252→ data_dict = parse_csv_row(line, keys)\n 253→ if not data_dict:\n 254→ continue\n 255→\n 256→ actual_name = data_dict[\"global_var_name\"].replace(\"\\\"\", \"\")\n 257→ if (actual_name == var_name_only\n 258→ or (less_strict and var_name_only in actual_name)):\n 259→ return data_dict\n 260→\n 261→ if not less_strict:\n 262→ return self.get_global_var(curr_db, global_var_name, True)\n 263→ else:\n 264→ return (\n 265→ f\"Global var '{global_var_name}' not found. \"\n 266→ \"Could it be a macro or should you use another tool?\"\n 267→ )\n 268→\n 269→\n 270→ def get_class(\n 271→ self,\n 272→ curr_db: str,\n 273→ class_name: str,\n 274→ less_strict: bool = False\n 275→ ) -> Union[str, Dict[str, str]]:\n 276→ \"\"\"\n 277→ Return class info (type, class_name, file, start_line, end_line, simple_name)\n 278→ from Classes.csv for class_name. If not found, tries partial match if less_strict is True.\n 279→\n 280→ Args:\n 281→ curr_db (str): Path to current CodeQL database folder.\n 282→ class_name (str): The name of the class/struct/union to find.\n 283→ less_strict (bool, optional): If True, use partial matching.\n 284→\n 285→ Returns:\n 286→ Union[str, Dict[str, str]]:\n 287→ - A dict with keys ['type','class_name','file','start_line','end_line','simple_name']\n 288→ - or an error message string if not found.\n 289→ \n 290→ Raises:\n 291→ CodeQLError: If Classes CSV file cannot be read (not found, permission denied, etc.).\n 292→ \"\"\"\n 293→ classes_file = Path(curr_db) / \"Classes.csv\"\n 294→ keys = [\"type\", \"class_name\", \"file\", \"start_line\", \"end_line\", \"simple_name\"]\n 295→ class_name_only = class_name.split(\"::\")[-1]\n 296→\n 297→ for row in self._iter_csv_lines(classes_file, \"Classes CSV\"):\n 298→ if class_name_only in row:\n 299→ row_dict = parse_csv_row(row, keys)\n 300→ if not row_dict:\n 301→ continue\n 302→\n 303→ actual_class = row_dict[\"class_name\"].replace(\"\\\"\", \"\")\n 304→ simple_class = row_dict[\"simple_name\"].replace(\"\\\"\", \"\")\n 305→ if (\n 306→ actual_class == class_name_only\n 307→ or simple_class == class_name_only\n 308→ or (less_strict and class_name_only in actual_class)\n 309→ or (less_strict and class_name_only in simple_class)\n 310→ ):\n 311→ return row_dict\n 312→\n 313→ if not less_strict:\n 314→ return self.get_class(curr_db, class_name, True)\n 315→ else:\n 316→ return f\"Class '{class_name}' not found. Could it be a Namespace?\"\n 317→\n 318→\n 319→ def get_caller_function(\n 320→ self,\n 321→ function_tree_file: str,\n 322→ current_function: Dict[str, str]\n 323→ ) -> Union[str, Dict[str, str]]:\n 324→ \"\"\"\n 325→ Return the caller function from function_tree_file that calls current_function.\n 326→\n 327→ Args:\n 328→ function_tree_file (str): Path to FunctionTree.csv.\n 329→ current_function (Dict[str, str]): The function dictionary whose caller we want.\n 330→\n 331→ Returns:\n 332→ Union[str, Dict[str, str]]:\n 333→ - Dict describing the caller if found\n 334→ - or an error string if the caller wasn't found.\n 335→ \n 336→ Raises:\n 337→ CodeQLError: If function tree file cannot be read (not found, permission denied, etc.).\n 338→ \"\"\"\n 339→ keys = [\"function_name\", \"file\", \"start_line\", \"function_id\", \"end_line\", \"caller_id\"]\n 340→ caller_id = current_function[\"caller_id\"].replace(\"\\\"\", \"\").strip()\n 341→\n 342→ for line in self._iter_csv_lines(function_tree_file, \"Function tree file\"):\n 343→ if caller_id in line:\n 344→ data_dict = parse_csv_row(line, keys)\n 345→ if not data_dict:\n 346→ continue\n 347→ if data_dict[\"function_id\"].replace(\"\\\"\", \"\").strip() == caller_id:\n 348→ return data_dict\n 349→\n 350→ # Fallback if 'caller_id' is in format file:line\n 351→ maybe_line = caller_id.split(\":\")\n 352→ if len(maybe_line) == 2:\n 353→ file_part, line_part = maybe_line\n 354→ function = self.get_function_by_line(function_tree_file, file_part[1:], int(line_part))\n 355→ if function:\n 356→ return function\n 357→\n 358→ return (\n 359→ \"Caller function was not found. \"\n 360→ \"Make sure you are using the correct tool with the correct args.\"\n 361→ )\n 362→\n 363→\n 364→ def extract_function_lines_from_db(\n 365→ self,\n 366→ db_path: str,\n 367→ current_function: Dict[str, str],\n 368→ ) -> Tuple[str, int, int, List[str]]:\n 369→ \"\"\"\n 370→ Extract function lines from the CodeQL database source archive.\n 371→\n 372→ Args:\n 373→ db_path (str): Path to the CodeQL database directory.\n 374→ current_function (Dict[str, str]): The function dictionary.\n 375→\n 376→ Returns:\n 377→ Tuple[str, int, int, List[str]]:\n 378→ - file_path (str): The file path (after .replace and [1:])\n 379→ - start_line (int): Starting line number\n 380→ - end_line (int): Ending line number\n 381→ - all_lines (List[str]): Full file splitlines\n 382→ \"\"\"\n 383→ src_zip = Path(db_path) / \"src.zip\"\n 384→ file_path = current_function[\"file\"].replace(\"\\\"\", \"\")[1:]\n 385→ code_file = read_file_lines_from_zip(str(src_zip), file_path)\n 386→ lines = code_file.split(\"\\n\")\n 387→\n 388→ start_line = int(current_function[\"start_line\"])\n 389→ end_line = int(current_function[\"end_line\"])\n 390→ return file_path, start_line, end_line, lines\n 391→\n 392→\n 393→ @staticmethod\n 394→ def format_numbered_snippet(file_path: str, start_line: int, snippet_lines: List[str]) -> str:\n 395→ \"\"\"\n 396→ Format a code snippet with line numbers.\n 397→\n 398→ Args:\n 399→ file_path (str): Path to the source file.\n 400→ start_line (int): Starting line number (1-indexed).\n 401→ snippet_lines (List[str]): The code lines to format.\n 402→\n 403→ Returns:\n 404→ str: Formatted snippet with line numbers.\n 405→ \"\"\"\n 406→ snippet = \"\\n\".join(\n 407→ f\"{start_line + i}: {text}\" for i, text in enumerate(snippet_lines)\n 408→ )\n 409→ return f\"file: {file_path}\\n{snippet}\"\n 410→\n\n\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n\n"}]},"uuid":"e8b437ad-9c36-481b-925a-4306b1c67a8f","timestamp":"2026-02-11T12:34:13.777Z","sourceToolAssistantUUID":"def8221f-556c-42c7-a7d6-c840a93754f3"}
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{"parentUuid":"0b1cc8c1-e341-48af-9214-1fcd03d2f2a3","isSidechain":true,"userType":"external","cwd":"/home/josie/development/personal/Vulnhalla","sessionId":"b8fa86be-f37b-48ee-ad6c-420dbe158ef1","version":"2.1.38","gitBranch":"main","agentId":"a9b8fb5","slug":"synthetic-yawning-mountain","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01E8AkzpX1RiwT8NQs2tSsxZ","type":"tool_result","content":" 1→# Vulnhalla \n 2→# Automated CodeQL Analysis with LLM Classification\n 3→\n 4→\n 5→
![\"Vulnhalla\"](\"images/vulnhalla_logo.png\")
\n 6→