Free when a stranger calls software for linux

The Examiner is an application that utilizes the objdump command to disassemble and comment foreign executable binaries. This app was designed to analyze static compiled binaries but works ok with others. The intention is for forensic research but could also be used in general reverse engineering.
This program can only handle basic dissassembly. If the binary has been modified to resist debugging then the Examinier probably will not be able to analyze the code. Also the Examiner will not analyze live running code. This can be a good thing but if you need to look at code when it runs or deal with complicated disassembly you should probably use Fenris.
Main features:
- Automates objdump usage
- Can generate cross-reference files of functions, interrupts and other useful things
- Locates functions within the binary
- Understands the stack and comments on its state
- Can parse and understand the contents of the .rodata section
- Cross references .rodata calls and comments on them
- Locates .data pointer references to .rodata
- Provides an easy to read CALL syntax for comments
- Understands and looks up interrupts calls
- Utilizes Linux source headers to determine function names based on what interrupt is called
- Can differentiate all of the socketcall functions
- Can comment on some C like constants for function calls
- Separates functions based on ret calls
- Can recognize and attempts to decode UPX compressed binaries
- Works with TCT and Fenris dress utility
- Can detect crippled ELF executables and burneye executables
- Recognizes symbols and will cross-reference dynamic libraries
Enhancements:
- Has rudementary detection of burneye via 7350 sig.
- Can detect crippled ELF header files (optionally uncripple)
- Added a TUTORIAL file
- Modified default working dir to $HOMEexaminer-data
- Can cross-reference .data pointers to .rodata sections
- Now records pushl calls
- Fixed -H to dump headers instead of -R
- Added -o to specify an output file or STDOUT with -
- Added -c to specify a comment character
- Added a new util xhierarchy to print function call hierarchy

washerDryer keeps track of the clothes in your washing machine and dryer, and alerts you when they are ready. Keeps track of up to 10 loads at once, and can alert you in a multitude of ways.
Enhancements:
- Added feature to change process times on the fly.
- Configuration window no longer automatically closes.
- Replaced strcpy calls with strncpy.

Sys::AlarmCall is a package to handle the logic in timing out calls with alarm() and an ALRM handler, allowing nested calls as well.

SYNOPSIS

use Sys::AlarmCall;

$result = alarm_call($timeout1,$func1,@args1);
@result = alarm_call($timeout2,$func2,@args2);

Sys::AlarmCall provides a straightforward function call to use the alarm handler. It also handles the logic which allows nested time out calls if timeout calls are run thorugh the alarm_call() functions.

The main advantages of Sys::AlarmCall are that:

1. simple calls, e.g.

@result = &func(@args); #Normal function call with &
$result = func(@args);; #Normal function call
@result = &$code_reference(@args);
@result = $obj->func(@args); #Object method call

become simple calls:

@result = alarm_call($timeout,&func,@args);
$result = alarm_call($timeout,func,@args);
@result = alarm_call($timeout,$code_reference,@args);
@result = alarm_call($timeout,->func,$obj,@args);

Automated support for compound RPC calls is a project which augments RPCGEN to support NFSv4-style compound procedures.

NFSv4 specifies that the RPC calls be batched into a "compound" call. There is no support for this in RPCGEN.

By rearranging the ONC IDL for NFSv4 into AutoGen definitions, these templates will emit the original IDL *plus* all the code to package, send, distribute, collect, return, and dispatch the results.

The distributed program author merely needs to call and supply server procedures for the routines specified in the IDL.

Templates for these calls and service routines is provided, too. The NFSv4 definitions are included.

Python Call Graph (pycallgraph) is a Python library that creates call graphs for Python programs

pycallgraph is quite new. It seems to work well and I will most likely improve it in the near future!

Quantum::Entanglement package contains QM entanglement of variables in perl.

SYNOPSIS

use Quantum::Entanglement qw(:DEFAULT :complex :QFT);

my $c = entangle(1,0,i,1); # $c = |0> + i|1>
my $d = entangle(1,0,1,1); # $d = |0> + |1>

$e = $c * $d; # $e now |0*0> + i|0*1> + |1*0> + i|1*1>, connected to $c, $d

if ($e == 1) { # observe, probabilistically chose an outcome
# if we are here, ($c,$d) = i|(1,1)>
print "* $e == 1n";
}
else { # one of the not 1 versions of $e chosen
# if we are here, ($c,$d) = |(0,0)> + i|(1,0)> + |(0,1)>
print "* $e != 1n";
}

BACKGROUND

"Quantum Mechanics - the dreams that stuff is made of."

Quantum mechanics is one of the stranger things to have emerged from science over the last hundred years. It has led the way to new understanding of a diverse range of fundamental physical phenomena and, should recent developments prove fruitful, could also lead to an entirely new mode of computation where previously intractable problems find themselves open to easy solution.

While the detailed results of quantum theory are hard to prove, and even harder to understand, there are a handful of concepts from the theory which are more easily understood. Hopefully this module will shed some light on a few of these and their consequences.

One of the more popular interpretations of quantum mechanics holds that instead of particles always being in a single, well defined, state they instead exist as an almost ghostly overlay of many different states (or values) at the same time. Of course, it is our experience that when we look at something, we only ever find it in one single state. This is explained by the many states of the particle collapsing to a single state and highlights the importance of observation.

In quantum mechanics, the state of a system can be described by a set of numbers which have a probability amplitude associated with them. This probability amplitude is similar to the normal idea of probability except for two differences. It can be a complex number, which leads to interference between states, and the probability with which we might observe a system in a particular state is given by the modulus squared of this amplitude.

Consider the simple system, often called a qubit, which can take the value of 0 or 1. If we prepare it in the following superposition of states (a fancy way of saying that we want it to have many possible values at once):

particle = 1 * (being equal to 1) + (1-i) * (being equal to 0)

we can then measure (observe) the value of the particle. If we do this, we find that it will be equal to 1 with a probability of

1**2 / (1**2 + (1-i)(1+i) )

and equal to zero with a probability of

(1+i)(1-i) / (1**2 + (1-i)(1+i) )

the factors on the bottom of each equation being necessary so that the chance of the particle ending up in any state at all is equal to one.

Observing a particle in this way is said to collapse the wave-function, or superposition of values, into a single value, which it will retain from then onwards. A simpler way of writing the equation above is to say that

particle = 1 |1> + (1-i) |0>

where the probability amplitude for a state is given as a multiplier of the value of the state, which appears inside the | > pattern (this is called a ket, as sometimes the bra or < |, pattern appears to the left of the probability amplitudes in these equations).

Much of the power of quantum computation comes from collapsing states and modifying the probability with which a state might collapse to a particular value as this can be done to each possible state at the same time, allowing for fantastic degrees of parallelism.

Things also get interesting when you have multiple particles together in the same system. It turns out that if two particles which exist in many states at once interact, then after doing so, they will be linked to one another so that when you measure the value of one you also affect the possible values that the other can take. This is called entanglement and is important in many quantum algorithms.

Devel::CallerItem is an Perl object representing a function call from the stack of function calls.

SYNOPSIS

Usage:

require Devel::CallerItem;

$call = Devel::CallerItem->from_depth($depth) || return;
$passed_arguments_ref = $call->argument_list_ref();
$callpack = $call->pack();
$callfile = $call->file();
$callline = $call->line();
$callsub = $call->subroutine();
$bool = $call->has_args();
$bool = $call->wants_array();
($arg_ref,@caller) = $call->as_array();
$call_string = $call->as_string($print_level);
$passed_arguments_string = $call->arguments_as_string();

$printable_arg = Devel::CallerItem->printable_arg($arg,$print_level);

Devel::CallerItem objects hold all the information about a specific function call on the stack. The information is basically that obtained from caller and @DB::args, packaged into an object. This comes with some useful methods to print the object in a nice way.

phpCallLog is a Web based application that is designed to help keep track of phone calls and messages.

It is powered by PHP and MySQL, and is platform independent. phpCallLog was intended for support calls that require followup calls, or where multiple people will handle a single issue. It can be run on a public server, so that if a company wants to allow their customers to fill out a support form, it will email the techs.

There is a login required to see the information thats been submitted. It also allows for sending messages between techs.